Time series is different from more traditional classification and regression predictive modeling problems.
The temporal structure adds an order to the observations. This imposed order means that important assumptions about the consistency of those observations needs to be handled specifically.
For example, when modeling, there are assumptions that the summary statistics of observations are consistent. In time series terminology, we refer to this expectation as the time series being stationary.
These assumptions can be easily violated in time series by the addition of a trend, seasonality, and other time-dependent structures.
In this tutorial, you will discover how to check if your time series is stationary with Python.
How to identify obvious stationary and non-stationary time series using line plot.
How to spot check summary statistics like mean and variance for a change over time.
How to use statistical tests with statistical significance to check if a time series is stationary.
Update Feb/2017: Fixed typo in interpretation of p-value, added bullet points to make it clearer.
Updated May/2018: Improved language around reject vs fail to reject of statistical tests.
The observations in a stationary time series are not dependent on time.
Time series are stationary if they do not have trend or seasonal effects. Summary statistics calculated on the time series are consistent over time, like the mean or the variance of the observations.
When a time series is stationary, it can be easier to model. Statistical modeling methods assume or require the time series to be stationary to be effective.
Below is an example of the Daily Female Births dataset that is stationary.
Running the example creates the following plot.
Observations from a non-stationary time series show seasonal effects, trends, and other structures that depend on the time index.
Summary statistics like the mean and variance do change over time, providing a drift in the concepts a model may try to capture.
Classical time series analysis and forecasting methods are concerned with making non-stationary time series data stationary by identifying and removing trends and removing seasonal effects.
Below is an example of the Airline Passengers dataset that is non-stationary, showing both trend and seasonal components.
The notion of stationarity comes from the theoretical study of time series and it is a useful abstraction when forecasting.
Stationary Process: A process that generates a stationary series of observations.
Stationary Model: A model that describes a stationary series of observations.
Trend Stationary: A time series that does not exhibit a trend.
Seasonal Stationary: A time series that does not exhibit seasonality.
Strictly Stationary: A mathematical definition of a stationary process, specifically that the joint distribution of observations is invariant to time shift.
Should you make your time series stationary?
If you have clear trend and seasonality in your time series, then model these components, remove them from observations, then train models on the residuals.
If we fit a stationary model to data, we assume our data are a realization of a stationary process. So our first step in an analysis should be to check whether there is any evidence of a trend or seasonal effects and, if there is, remove them.
— Page 122, Introductory Time Series with R.
Statistical time series methods and even modern machine learning methods will benefit from the clearer signal in the data.
We turn to machine learning methods when the classical methods fail. When we want more or better results. We cannot know how to best model unknown nonlinear relationships in time series data and some methods may result in better performance when working with non-stationary observations or some mixture of stationary and non-stationary views of the problem.
The suggestion here is to treat properties of a time series being stationary or not as another source of information that can be used in feature engineering and feature selection on your time series problem when using machine learning methods.
There are many methods to check whether a time series (direct observations, residuals, otherwise) is stationary or non-stationary.
Look at Plots: You can review a time series plot of your data and visually check if there are any obvious trends or seasonality.
Summary Statistics: You can review the summary statistics for your data for seasons or random partitions and check for obvious or significant differences.
Statistical Tests: You can use statistical tests to check if the expectations of stationarity are met or have been violated.
Above, we have already introduced the Daily Female Births and Airline Passengers datasets as stationary and non-stationary respectively with plots showing an obvious lack and presence of trend and seasonality components.
Next, we will look at a quick and dirty way to calculate and review summary statistics on our time series dataset for checking to see if it is stationary.
A quick and dirty check to see if your time series is non-stationary is to review summary statistics.
You can split your time series into two (or more) partitions and compare the mean and variance of each group. If they differ and the difference is statistically significant, the time series is likely non-stationary.
Next, let's try this approach on the Daily Births dataset.
Because we are looking at the mean and variance, we are assuming that the data conforms to a Gaussian (also called the bell curve or normal) distribution.
We can also quickly check this by eyeballing a histogram of our observations.
Running the example plots a histogram of values from the time series. We clearly see the bell curve-like shape of the Gaussian distribution, perhaps with a longer right tail.
Next, we can split the time series into two contiguous sequences. We can then calculate the mean and variance of each group of numbers and compare the values.
Running this example shows that the mean and variance values are different, but in the same ball-park.
Next, let's try the same trick on the Airline Passengers dataset.
Cutting straight to the chase, we can split our dataset and calculate the mean and variance for each group.
Running the example, we can see the mean and variance look very different.
We have a non-stationary time series.
Let's take one step back and check if assuming a Gaussian distribution makes sense in this case by plotting the values of the time series as a histogram.
Running the example shows that indeed the distribution of values does not look like a Gaussian, therefore the mean and variance values are less meaningful.
This squashed distribution of the observations may be another indicator of a non-stationary time series.
Reviewing the plot of the time series again, we can see that there is an obvious seasonality component, and it looks like the seasonality component is growing.
This may suggest an exponential growth from season to season. A log transform can be used to flatten out exponential change back to a linear relationship.
Below is the same histogram with a log transform of the time series.
Running the example, we can see the more familiar Gaussian-like or Uniform-like distribution of values.
We also create a line plot of the log transformed data and can see the exponential growth seems diminished, but we still have a trend and seasonal elements.
We can now calculate the mean and standard deviation of the values of the log transformed dataset.
Running the examples shows mean and standard deviation values for each group that are again similar, but not identical.
Perhaps, from these numbers alone, we would say the time series is stationary, but we strongly believe this to not be the case from reviewing the line plot.
This is a quick and dirty method that may be easily fooled.
We can use a statistical test to check if the difference between two samples of Gaussian random variables is real or a statistical fluke. We could explore statistical significance tests, like the Student t-test, but things get tricky because of the serial correlation between values.
In the next section, we will use a statistical test designed to explicitly comment on whether a univariate time series is stationary.
Statistical tests make strong assumptions about your data. They can only be used to inform the degree to which a null hypothesis can be rejected or fail to be reject. The result must be interpreted for a given problem to be meaningful.
Nevertheless, they can provide a quick check and confirmatory evidence that your time series is stationary or non-stationary.
The Augmented Dickey-Fuller test is a type of statistical test called a unit root test.
The intuition behind a unit root test is that it determines how strongly a time series is defined by a trend.
There are a number of unit root tests and the Augmented Dickey-Fuller may be one of the more widely used. It uses an autoregressive model and optimizes an information criterion across multiple different lag values.
The null hypothesis of the test is that the time series can be represented by a unit root, that it is not stationary (has some time-dependent structure). The alternate hypothesis (rejecting the null hypothesis) is that the time series is stationary.
Null Hypothesis (H0): If failed to be rejected, it suggests the time series has a unit root, meaning it is non-stationary. It has some time dependent structure.
Alternate Hypothesis (H1): The null hypothesis is rejected; it suggests the time series does not have a unit root, meaning it is stationary. It does not have time-dependent structure.
We interpret this result using the p-value from the test. A p-value below a threshold (such as 5% or 1%) suggests we reject the null hypothesis (stationary), otherwise a p-value above the threshold suggests we fail to reject the null hypothesis (non-stationary).
p-value > 0.05: Fail to reject the null hypothesis (H0), the data has a unit root and is non-stationary.
p-value <= 0.05: Reject the null hypothesis (H0), the data does not have a unit root and is stationary.
Below is an example of calculating the Augmented Dickey-Fuller test on the Daily Female Births dataset. The statsmodels library provides the adfuller() function that implements the test.
Running the example prints the test statistic value of -4. The more negative this statistic, the more likely we are to reject the null hypothesis (we have a stationary dataset).
As part of the output, we get a look-up table to help determine the ADF statistic. We can see that our statistic value of -4 is less than the value of -3.449 at 1%.
This suggests that we can reject the null hypothesis with a significance level of less than 1% (i.e. a low probability that the result is a statistical fluke).
Rejecting the null hypothesis means that the process has no unit root, and in turn that the time series is stationary or does not have time-dependent structure.
We can perform the same test on the Airline Passenger dataset.
Running the example gives a different picture than the above. The test statistic is positive, meaning we are much less likely to reject the null hypothesis (it looks non-stationary).
Comparing the test statistic to the critical values, it looks like we would have to fail to reject the null hypothesis that the time series is non-stationary and does have time-dependent structure.
Let's log transform the dataset again to make the distribution of values more linear and better meet the expectations of this statistical test.
Running the example shows a negative value for the test statistic.
We can see that the value is larger than the critical values, again, meaning that we can fail to reject the null hypothesis and in turn that the time series is non-stationary.
In this tutorial, you discovered how to check if your time series is stationary with Python.
The importance of time series data being stationary for use with statistical modeling methods and even some modern machine learning methods.
How to use line plots and basic summary statistics to check if a time series is stationary.
How to calculate and interpret statistical significance tests to check if a time series is stationary.
Do you have any questions about stationary and non-stationary time series, or about this post?
Just a quick question, when testing the residuals of an OLS regression between two price SERIES for stationarity would you consider then, the two price series, to be cointegrated If the H0 was rejected by the ADF test that you ran on the residuals ?
Or would you first run the ADF test on each of the price series in order to see if they are I(1) themselves?
Both. I would check both the input data and the residuals.
I asked this because of a "common sense" (maybe not) assumption that price series would not be, per se, stationary, by definition, so, sometimes I ask myself if isn't this kind of testing a little too much.
I'm high on ML methods for time series over linear methods like ARIMA, but one really important consideration is stationarity.
Trend removal and exploring seasonality specifically is a big deal otherwise ML methods blow-up for the same reasons as linear methods.
I'd like to do a whole series of posts on stationarity.
I would like to forcats time series via RNN, but for getting more accuarate results I need at the beging check all this 3 characteristics.
Data visualization is a good start.
beside using log, do you consider to use panda.diff()?
Thanks Cipher, it beats doing the difference manually.
great article! easy to understand. simplicity is powerful. and all those good stuff.
Thanks Seine. I'm glad you found it useful.
Yes, that is a typo, fixing now and I made it clearer with some more bullet points. All of the analysis in the post is correct.
In summary, the null hypothesis (H0) is that there is a unit root (autoregression is non-stationary). Rejecting it means no unit root and non-stationary.
A p-value more than the critical value means we cannot reject H0, we accept that there is a unit root and that the data is non-stationary.
No problem. It is confusing.
So my time series are stationary. In my example, I have 525600 values giving me a maxlag of 102. These are minute data for one month. But I don't understand how so few lags can detect e.g a daily variation?
Now when I calculate the distribution of occurrence frequency, there is clearly a time dependence on UT on hourly binned data. I have a higher number of samples, for a certain range of values, at around 15 UT compared to other times. So I have a UT dependence in my data, but it is still stationary. So I guess one should be careful when using this test. In my case there is a UT dependency on the number of values, at a certain level, rather than the value itself. How to deal with this? One idea, perhaps, is to add sine and cosine of time to the inputs. Any comments on this?
Interesting. Perhaps it would be worth performing a stationary test at different time scales?
Now, I am really confused. I also did a test on artificial data from a sine function with normally distributed data added to it. Now the test gave a p-value of 0.07, but from the plot it was very obvious the data is non-stationary. So I really suggest to use the group by process in Pandas and plot the data.
Another approach, instead of removing seasonality is the following. If only the target values, used for training a prediction model, are non-stationary, then it might be easier to add sine/cosine of time to the inputs. Of course, the input space increases but there is no need to create time-lagged data for these inputs.
I appreciate any comments and suggestions.
I'm dubious about your results.
I have found the test to be reliable.
Perhaps the version of the statsmodels library is out of date, or perhaps the data you have loaded does not match your expectation?
I was having the same problem, but changing the printing format, fixed it for me.
Hi , I want to forecast temperature of my time series dataset. Dickey -Fuller test in python gives me above results, which shows Test statistics is larger than any of the critical value meaning time series is not stationary after taking transformations. So ,can i forecast without time series being non-stationary?
You can, but consider another round of differencing.
Does the statsmodel python library require us to convert the series into stationary series before feeding the series to any of the ARMA or ARIMA models ?
Ideally, I would. The model can difference to address trends, but I would recommend explicitly pre-processing the data before hand. This will help you better understand your problem/data.
Great article, you make these topics understandable.
I started testing some series for stationarity and got strange behaviors I cannot understand.
give ADF statistics of +2.59 and -4.23.
I'd expect both results to be very close to each other. Neither is stationary series is stationary as the express the same trend. But the test is positive in one case and negative in the other.
I would not worry, focus on the test (e.g. the value relative to critical value), not the value itself.
But this is precisely my problem: with a slight change in the number of observations of a series of constant slope/trend of +1, the test swings entirely from non-stationary to stationary for a reason I fail to understand.
Ah I see. It might be a case of requiring a critically minimum amount of data for the statistical test to be viable.
quick questions if you don't mind: I would like to test a few trading strategies on ETFs. It looks obvious that these time series are non stationary.
how does one go about converting them to stationary?
I would like to use Technical Indicators (which input are prices) as features in my model. What shall I do to the features?
my objective is not to predict price but to classify into "buy/sell" (or hold).
any algo better suited for financial time series?
You can use differencing and seasonal adjustment. I have posts on both methods, use the search feature.
Actually, when ADF Statistic < critical value then it is stationary. Comparing pvalue with critical value is not right and confusing. Including the adfuller api explanation in http://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.adfuller.html.
I don't think we are comparing the p-value to anything in this post, I believe are reviewing the test statistic.
I see. It is ADF Statistic < critical value or p-value < threshold, then the series is stationary. Threshold is 0.05 etc.
Perhaps I'm dense, but where exactly? Can you quote the text?
I note that I describe how to interpret p-values separately from interpreting test static.
And there is another explanation based on critical value.
So there are two ways of considering the adf result, using p-value or using critical value.
In that section, I was introducing the meaning of the p-value, not how to interpret the test. Sorry for the confusion.
I performed the Dickey–Fuller test and get 1 as p-value. Then I performed Box_Cox transform which allowed to decrease p-value to 0.96. Then I performed seasonal differentiation and p-value decreased to 0.0000. After this, I build LSTM neural network and train it. Now, I want to compare results in the original scale, in the transformed. I found the scipy.special.inv_boxcox() function, which does the inverse transformation. But for me, it is not working. What can be wrong?
Perhaps you can experiment on some test data separate from your model. Transform and then inverse transform.
Remember all operations need to be reversed, including the seasonal adjustment.
I'm very interested in your articles Jason, I have a question.
If I train my model using the residual data ( removing seasonal and trends), what about the predicted values how we get the correct values ( how to add seasonally and trends again) .
I hope that I can present my question correctly. sorry for my poor English.
If you remove the trend and seasonality prior to modeling, you can add them back to the prediction.
If you used differencing, invert the differencing. If you used a model, invert the application of the model.
I have many examples on the blog of this.
I am looking for he blog posts noted above regarding how to re-add trends and seasonality to modeling after it has been removed. Could you please point me to the blog posts which cover this? I cannot seem to locate them.
If you remove the trend by differencing or the seasonality by seasonal differencing, you can add it back directly, via addition with the value that was subtracted.
Hi Jason, as far as I know adfuller is a statistical test for Random walk, H1 means not Random walk, above you revealing that H1 also means stastionary time-series, does any non-random walk is stationary?
A random walk is not stationary.
I think it is worth mentioning that to apply the ADF test, we assume that the time series follows a given model (dy(t)=cst+gamma*y(t-1)+….+e(t)) where e(t) is the error term that is supposed to be a white noise.
Nice tutorial! I'm just starting out with time series data so I'm wondering, if my data doesn't pass stationarity tests, then I cannot use time series analysis on it, is that right? Can RNNs model time series that are non-stationary?
You can, but results might not be good.
RNNs do seem to perform better with a stationary series in my experience.
I guess trend-stationary series is the one that has a trend but no unit root. Correct me if I am wrong .
I have a small question, when you are working with ADF when the result suggests stationary is it difference stationary, stationary in increments or normal weak stationary in time series…….
The statistical test is reporting a likelihood of being stationary rather than a fact.
I am working with a time series that has multiple random measurements for every moment in time. In other words, my X series includes sets of distinct measured values for every timestamp.
Will the described procedure and code work for me as is? Should I be sorting such X or not?
You would need to work with each time series (variable) separately.
Hi Jason. In the example above you used ADF to test whether the Gaussian normally distributed sample is stationary. 1) Any hints on what to do if we try to model a process that shows a non-Gaussian distribution? 2) Can we still make inferences about stationarity based on means/variances of two subsamples from a non-Gaussian process? 3) Could you please point me to a reference with a nice description of how to test for stationarity in non-normal samples and how to model such time series? Thanks!
Good question, using a data visualization is always a great fall-back.
Hi, this is a very interesting tutorial. Thanks a lot.
I am having about 1000+ different time-series dataset in the format of (year,number) and need to forecast the values for each and every dataset in next 5 years.As i have lot of datasets, I would like to know if there is a way to automate the aforementioned stationary check step, so that I can directly perform the ARIMA process? or is there any other algorithm that you would recommend?
Perhaps difference all datasets before modeling?
Thanks a lot for the suggestion. Did you mean performing 'log' as 'difference'? And after that using the p-value of Augmented Dickey-Fuller test to decide the stationary?
Just curious to know, does performing 'log' guarantee that you have a stationary time-series dataset?
Log and other power transforms can calm an increasing/changing variance and make the data distribution more Gaussian.
What do you mean it does not work?
The turth is that the time series is still non-stationary.How can we get the stationary time series ?
You can remove the trend by differencing, you can remove the seasonality by seasonal differencing.
However could you please advice how would you approach stationarity test with multivariate, multiinput and multioutput time series?
I'm working on multistep prediction for a few thousands of different elements. For each element, i'll probably have to use a few different kind of measurements (like temperature, pressure, traffic etc.). I have to predict three timeseries for each site. For some network elements measurements goes up, for others they go down… How should i approach stationarity test properely in that case? Should i check and transform for each element separately? Or perform the stationarity test for each aggregated by time measurement (over the whole set of elements)? What do you suggest?
I would start by performing a test on each separate univariate series.
Then test if making one or all series stationary impacts model performance, e.g a linear model.
I have a question related to the Augmented Dickey-Fuller (ADF) test that you applied with the "International airline passengers" dataset. When ADF test is applied using R we get different decision results than when using python. Here are the numbers.
Conclusion: Reject Ho. So we accept it is stationary.
The test statistic is positive, meaning we are much less likely to reject the null hypothesis (it looks non-stationary).
Both datasets (R: AirPassengers and Python: daily-total-female-births) are the same. So, I can not get the reasons why ADF test showed different results.
Would you please give me a hand in finding an explanation of this rare situation?
The airline dataset is not stationary. If a library reports that it is, perhaps there is a bug in your code or the library?
I run the test again but now using your dataset as a source (converting it to" ts" class in R) and I got the same result.
Anyway, there is something definitely wrong in this way to present the stationarity study of this dataset by using the Augmented Dickey-Fuller (ADF) test in R.
There are many articles on the web (www) that using just the ADF test with R conclude that the AirPassengers dataset is stationary when clearly is not.
I agree with you 100%. I mean, considering R if we complement our study using other test and Autocorrelation analysis we can get the same conclusion that the AirPassengers dataset is not a stationary one.
Thanks for your answer and time, of course.
Interesting. Perhaps there is a bug in the routine, or a change in the way the test must be interpreted in R? I think the latter is more likely.


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redpajama_set_name: RedPajamaGithub


The British Society of Rheumatology has produced a set of NICE accredited guidelines for the use of biologic therapies in patients with inflammatory arthritis.
It addresses safety recommendations for all biologic therapies approved by the National Institute for Health and Care Excellence (NICE) up to June 2016, for use in all inflammatory arthritides [RA, PsA and axial SpA (SpA) including AS].
These evidence based guidelines addressed biologic safety by asking about screening measures, monitoring, and comorbidity issues. Below are a sampling of several key recommendations - please consult the Rheumatology link provided for acomplete listing of guidelines.
Patients who have had previous inadequate treatment for active TB should be investigated for active TB.
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It's only 9:00 a.m., but Al Sparhawk is wide awake. Such is life when you're the father of a six-month-old. As young Hollis Mae Sparhawk lets out an exuberant squawk and her dad gushes on and on about the joys of fatherhood, it's clear the elder Sparhawk is no ordinary rock star. In fact it's hard to think of the music his band, Low, plays as having much to do with rock, let alone the trappings of the lifestyle. From its home base in Duluth, Minnesota, Low creates a minimalist, skeletal, slow sound some skeptics have deemed "snorecore". The group's no-frills instrumentation consists of Sparhawk's lone guitar; his wife Mimi Parker's snare drum, floor tom, and ride cymbal; and Zak Sally's bass.
Low, with Ida and A Kite Is a Victim: Underground Coffeeworks, 105 Narcissus Ave, West Palm Beach. 8:00 p.m. Saturday, October 14. Tickets for the show cost $7. Call 305-261-7585.
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Typical of Low, this progression has been a long time coming. The band's 1994 debut, I Could Live in Hope, was empty, spacious, and austere. The shimmering, echo-laden production came courtesy of producer-engineer Kramer, who'd lent essentially the same flavor to Galaxie 500. The cryptically lovely "Words" and transparently elegant reading of "(You Are My) Sunshine" mark that first chapter.
Next came Long Division, a darker, more downbeat effort that is equally spare. The standout track is "Violence", a Sparhawk-sung masterpiece with an agonizingly maudlin guitar line. By this time the band was actively plying its trade across the country, sometimes opening shows for bands less than completely compatible (such as Soul Coughing) and dealing with drunken crowds neither expecting nor desiring glacially paced starkness, enchanting two-part harmonies, and pin-drop volume. "We still have some people who get a little anxious," Sparhawk says, "but we're used to it by now. But if you're having a bad night, the last thing you need is someone yelling 'Free Bird' or 'Back in Black' or something." The trio eventually decided to accommodate the Skynyrd request, playing a drawn-out, morose version of the freedom-rock anthem. "Once in a while we wax nostalgic for the days when everybody hated us," Sparhawk says. Nowadays that's reversed itself: Bands trip over one another to open for Low and its small but fervent fan base. Sparhawk reports receiving plenty of fan mail, especially when Hollis was born. "We get letters from people who are very moved by what we do -- really personal, overwhelming ones with stories about how our music has been a part of people's lives," he says. "It's very flattering and humbling at the same time. To have a good handful of people who feel that way about us is probably the best evidence to me that what we're doing is worth doing."
The sound of shhh-ing is common these days at Low's concerts, where audiences expect and enforce silence. "It's definitely nice to be doing shows where people come and listen," offers the reserved, self-effacing Sparhawk. "We're able to do certain things that are very subtle, and we know that those things are being heard." He makes small talk with the crowd in a droll deadpan, while the stoic Parker stays quiet, except when she's angelically singing. Sally, who isn't religious, often will drink beer onstage or play with his back to the audience.
"We knew when we started that most people would not be that into what we were doing," he says, adding: "Actually part of the reason we started the band was to get that kind of reaction. We thought we'd piss people off with very slow, quiet, long songs -- just to annoy all these Johnny-come-lately alternative people. But it ended up being very fun to play, very satisfying and exciting to write songs using those elements."
In 1996 the band recorded the Transmission EP (centered around a decelerated cover of the classic Joy Division song of the same name) with Kramer and Steve Albini, who was busy making hit records with Nirvana and Bush. Not until 1997's Steve Fisk-produced The Curtain Hits the Cast came the first hints that Low might not be conceptually strapped to the slow, simple, and soft m.o. forever. "We have to change," Sparhawk says. "Most of our time and energy is spent trying to walk that thin line between redundancy and going out of our way for the sake of going out of our way." The shimmering "Over the Ocean", for instance, ventures into orchestral territory, and the nearly fifteen minutes of "Do You Know How to Waltz?" wanders through a perilous thicket of reverb. The band's label, Vernon Yard Recordings, dropped Low later that year but released a remix album titled Owl in 1998. As Sparhawk points out, the record was produced "with our approval but not with any of our involvement."
Turning Low's majestic hymns into dance trinkets is virtually a thankless task: Bronski Beat's Jimmy Somerville has his way with "Words", spinning it into a confectionary bauble, while Tranquility Bass gussies up "Over the Ocean" with hip-hopperies and horns. When Low resurfaced on Chicago indie label Kranky that same year with Songs for a Dead Pilot, Low took even more liberties with its formula. Adding judicious strokes of keyboards and strings, the band retained its powerful impact while expanding its palette. "Be There" is as icy as cracked water pipes in December, with an edgy sense of foreboding. Maybe northern Minnesota influences the chilly music and accounts for Low's economy of means? "We grew up there," Sparhawk says. "We don't know any different. But it is a pretty long, gray, brutal winter. We usually get ten days in row that are twenty below zero."
Secret Name from 1999 (again recorded with Albini) relies on strings for emotional drama once more, with the delicate "Two-Step" and Parker's heartbreaking "Weight of Water" ascending to the top of the band's canon of greats. Last winter Sparhawk and Parker played their trump card: Christmas, a seasonal gift containing faithful takes on "Silent Night" and the like as well as some heartfelt originals. They also dropped their guard, lyrically speaking. Whereas "Two-Step" began with the puzzling line, "And the light burns your skin/In a language you don't understand", the band swung to the opposite direction for Yuletide. "We are musically very naked," Sparhawk explains, "but we aren't really about opening our souls." Until Christmas, that is.
What band but Low could, without even the faintest whiff of irony, title a tune "If You Were Born Today (Song for the Little Baby Jesus)"? Sparhawk envisions the scenario thus: "They'd kill you by age eight/Never get the chance to say/Joy to the world/And peace on the earth." Utterly devoid of self-consciousness, the song is moving for believer and heathen alike. "When the Christmas record came out, that kind of opened the floodgates," Sparhawk admits. "I think a lot of people find it refreshing that we call a spade a spade and say, 'We're going to do some religious songs here, and it's because we mean it,' you know?"
"Mormons aren't notorious for being fringe artists," Sparhawk continues. "Of course Mick Ronson from the Spiders From Mars was the one I lean toward. I know when I went to [Brigham Young University], I wanted to go see something rebellious and contrary. Unfortunately most people who go out and see shows are trying to get away from the religious part, sad as it is. There's the religious side -- you go to church and everybody's trying to be very proper, clean living and all this stuff -- and then there's the music that you go home and listen to that doesn't have anything to do with the religion. I guess there's some people in the church who are excited about the fact that we're a bridge between those two worlds.
"It's always a struggle to be honest with yourself," he concludes. "But I don't feel like I have to listen to Donny and Marie to be a good Mormon." Hollis will thank him some day.


text:
Aliki Theofilopoulos Kiriakou  (born June 14, 1972) is a Greek-American executive producer, director, writer, animator and storyboard artist. She is well known for her work on television series Phineas and Ferb and Descendants: Wicked World.  She began her career on Disney films such as Hercules, Mulan, Fantasia 2000 and many more.

Biography
Theofilopoulos was born in La Jolla, California and raised in San Diego. She attended the University of Southern California and shortly after graduation, moved to Los Angeles.

Filmography
 Doug Unplugs (2020–present), executive producer
 Solar Opposites (2020–present), director
 Harvey Girls Forever! (2018–2020), voice actress (Chloe Claireson) supervising producer, director
 Descendants: Wicked World (2015), director
 Doctor Lollipop (2013), director
 Phineas and Ferb (2008–2014), voice actress (Mandy), storyboard artist, writer
 Random! Cartoons (2007), creator (Yaki & Yumi, Girls on the Go!), voice actress (Sweet Little Girl, Fortune Fish, Kid #1, Kelly, Mrs. Dusenberry, Crimson) title cards, voice director, co-producer, writer, storyboard artist, character designer
 Superior Defender Gundam Force (2004), voice actress (Zero, Bell Wood, Zakos)
 Home on the Range (2004), animator
 ChalkZone (2002–2004), storyboard revisionist, character designer, clean up artist
 Treasure Planet (2002), animator
 Atlantis: The Lost Empire (2001), animator
 Fantasia 2000 (1999), animator
 Tarzan (1999), animator
 Hercules (1997), animator

References

External links

Animation Insider: Aliki Theofilopoulos Grafft

1972 births
Living people
American voice actresses
American television writers
People from La Jolla, San Diego
American storyboard artists
American animators
American people of Greek descent
American women television writers
Screenwriters from California
21st-century American women

redpajama_set_name: RedPajamaWikipedia


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In special reports, this week's files cover: Alien Life in our Solar System, Underwater Bases, UFOs Imitate Aircraft, NSA Sees and Ears Everything, Does God Have an Air Force?, Moon and Anomaly.
Unidentified Aerial Phenomena sightings were reported over Arizona, California, Connecticut, Florida, Illinois, Indiana, Massachusetts, New Jersey, New York, Ohio, South Carolina, and Virginia.
Unidentified Aerial Phenomena sightings were reported over Argentina, Australia, Brazil, Canada, Italy, Korea, Mexico, Peru, Portugal, and England in the United Kingdom.
Molecular hydrogen, as found on Enceladus, is one of the essential parts of life on Earth.
The new findings came from the Cassini spacecraft, which flew through a huge plume of water that was being shot out of the surface of Enceladus. More than 100 individual geysers blast water ice, organic molecules and other material into space from the South Polar Region of Saturn's moon Enceladus, as seen here by NASA's Cassini spacecraft.
Credit: NASA/JPL/SSI As it did, the spacecraft took readings of the water and sent them back to Earth for further study.
That study found evidence of molecular hydrogen in those jets of vapor and particles, which come from the moon itself. That molecular hydrogen (H2) could only plausibly have come from hydrothermal reactions between hot rocks and water underneath the moon's icy crust, the researchers claim.
On Earth, that same process provides energy for the entire ecosystems of life found around hydrothermal vents.
That hydrogen serves as an especially good food, and some of the most ancient life forms on Earth use it for fuel. The kind of life that may live on Enceladus would likely eat that hydrogen and then release methane – which has also been found coming from the moon.
Further research found that the material Cassini flew through found large amounts not only of molecular hydrogen but also carbon dioxide. Together, those two ingredients are critical in a process known as methanogenesis — a reaction that sustains microbes in similar dark, undersea environments on Earth.
Scientists think that three ingredients are needed for life on a planet: water, organic molecules and a source of energy. The first two have been detected on Enceladus before, but the new discovery means that all three key components are there, with the addition of a fuel source for keeping that life alive.
Editor's Note; a sophisticated underwater civilization could have evolved on these water planets or moons that have now developed an ability to travel in space. The Sumerians claimed they were visited by space traveler Oannes who lived in the sea and provided advanced information and writing. Carl Sagan discussed the amazing evidence in Sumerian writing claiming that extraterrestrial intelligence came to Earth and provided advanced education to mankind. History Channel's investigative journalist Phillip Coppers claims the Babylonian tale of Oannes who lived in the sea is the "amongst the best evidence that we might have potentially been visited by extraterrestrials in prehistory. Both the Sumerians and the early Egyptians and the Babylonians who followed, say that their writing system wasn't "invented", but "given" to them by their gods, who came down to the earth from the sky in "fiery chariots" or "sky boats. Strange structures and roads exist on the bottom or Earth's oceans that were built by somebody?
Using Google maps the researcher came across these very strange tracks on the sea bed off the coast of Canada. Numerous UFOs are seen leaving the ocean and returning. There multiple tracks on the bottom of the ocean in area thousands of feet deep.
Dr. Bruce Cornet has graciously allowed me to publish some of his scientific findings about UFOs. He has two doctorate degrees in scientific fields. On 24 September 1992 between 6:10 pm and 7:20 pm Dr. Cornet was driving home from work at Lamont-Doherty Earth Observatory in Palisades, NY to Middletown, NY.
Sunset was quickly approaching as Dr. Bruce Cornet drove north towards his exit on the New York Throughway. It did not set before he saw something that completely challenged his senses. Off to his right between the highway and Bear Mountain, which parallels the highway, he saw a large jetliner approaching in the distance. The only problem was its altitude. It was flying below the top of Bear Mountain, which has a maximum elevation of only 300 feet above the valley floor.
As it approached him it flashed its landing lights several times – on, off, on, off, on, off. By now the jetliner was less than a half mile away, and it clearly had his attention. Then the aircraft began to bank to its right. It slowly crossed the highway just in front of him in plain view. It was not much higher than 200 feet above the highway as it crossed less than 500 feet in front of him. It just reached the other side of the highway as he passed it. It had the distinct shape of a Boeing 707, but was painted solid black. There were no markings on it whatsoever. Even the windows seemed to be blackened out. It did make a sound like a jetliner as it passed near him.
Let's here from the skeptics. What could explain a large 707 jetliner flying well below minimum altitude of 1,000 feet (FAA regulation outside airport control) and the pilot flashing his landing lights? Was the pilot in trouble? Was he trying to alert drivers on the highway as he attempted to make an emergency landing on the highway?
FAA regulation 91.515 Flight altitude rules: (a) Notwithstanding 91.119 and except as provided in paragraph (b) of this section, no person may operate an airplane under VFR at less than — (1) One thousand feet above the surface, or 1,000 feet from any mountain, hill, or other obstruction to flight, for day operations; and (2) The altitudes prescribed in 91.177 for night operations. (b) This section does not apply — (1) During takeoff or landing; (2) When a different altitude is authorized by a waiver to this section under subpart J of this part; or (3) When a flight is conducted under a special VFR weather minimum of 91.157 with an appropriate clearance from ATC.
FAA regulation 91.177 Minimum altitude for IFR operations. (a) Operation of aircraft at minimum altitudes. Except when necessary for takeoff and landing, no person may operate an aircraft under IFR below – (1) The applicable minimum altitudes prescribed in Parts 95 and 97 of this chapter; or (2) If no applicable minimum altitude is prescribed in those parts — (i) In the case of operations over an area designated a mountainous area in part 95, an altitude of 2,000 feet above the highest obstacle within a horizontal distance of 4 nautical miles from the course to be flown; or (ii) In any other case, an altitude of 1,000 feet above the highest obstacle within a horizontal distance of 4 nautical miles from the course to be flown (AIM/FAR 1994 airman's information manual / federal aviation regulations).
These were some of the thoughts running through Cornet's mind as he prepared to take his exit at the Harriman toll exchange. What disturbed him the most was his observation that there were no engine pods on the wings or body of the aircraft. He dismissed that observation as probably mistaken (it made no sense whatsoever, given that he heard the distinct sound of jet engines). He continued on his journey, now traveling west on Rte 17M. But no further than a couple miles from the toll booths he saw the same aircraft again. This second time it crossed the highway from left to right about 1/2 mile ahead of him. It was again very low to the ground, and travelling at a slow speed: It took about five seconds to cross the highway. He became very concerned, because there was now the distinct possibility that the pilot was in serious trouble and preparing to make a crash landing. Even though the area seemed under-developed, Cornet knew that just beyond the trees along the highway there were large residential communities. And several hills jutted up on both sides of the highway, making any low altitude flight there very hazardous.
Cornet continued to question his eyes regarding the apparent speed of the aircraft. It seemed to be going much too slow to stay in the air. The map below shows four locations of his truck when the AOP crossed in front of him over the highway.
About a mile or two further west on Rte 17M Cornet saw the aircraft again about 1/2 mile ahead of him. This third time it crossed the highway from right to left at a similar slow speed and low altitude. He gave a sigh of relief that it had not crashed into the residential community, but where was it going and why? It was now becoming more apparent to him that whatever this thing was, it was putting on a show just for him. No one in front of him or behind him on the highway had been there for all three shows. But it couldn't be targeting him, he thought. Who would ever believe him if he told such a wild story without evidence? Anybody knows that a Boeing 707 can't fly without engines, and it would drop out of the air like a rock much below a stall speed of about 150 knots.
Cornet drove another mile shaking his head and wondering if anyone else who saw this aircraft was asking the same questions. Then ahead of him there it was for the fourth time crossing the highway, but this time from left to right. He had a hard time staring at it, because the Sun was just beginning to touch the horizon directly behind the aircraft. The highway at this point was oriented exactly east-west. This would be the last time he saw the aircraft in daylight, but not the last time that evening. The best was yet to com.
http://www.sunstar-solutions.com/AOP/Boeing/boeing07.htm Apparently, it goes both ways. ET and humans both make copycat vehicles.
I was out in my backyard shooting Contrails at 1 pm, March 4, 2017 with my Canon Power Shot SX50 HS and saw the contrails thru the lens of my camera. I was shooting on continuous rapid fire at 10 pictures per second, shooting 70 pictures total.
I started shooting the entire contrail, that's when the aircraft started to appear. The Saucers only appeared on my P.C. after downloading the 70 images. I can hardly believe what I have captured on my camera. I have been capturing quite a few of these UFO's that Shape shift into an aircraft.
Since its inception in 1952, the National Security Agency (NSA) has been shrouded in secrecy; for years the White House denied its very existence. For the first time since 9/11 changed everything, cameras go beyond the closed doors of the NSA, deep inside the agency's over 2 million square foot top-secret compound. NSA works closely with Australia, Canada, New Zealand, and the United Kingdom exchanging secret intercepts.
Intelligence organization of the United States government, responsible for global monitoring, collection, and processing of information and data for foreign intelligence and counterintelligence purposes – a discipline known as signals intelligence (SIGINT). NSA is concurrently charged with protection of U.S. government communications and information systems against penetration and network warfare. Although many of NSA's programs rely on "passive" electronic collection, the agency is authorized to accomplish its mission through active clandestine means, among which are physically bugging electronic systems and allegedly engaging in sabotage through subversive software.
Moreover, NSA maintains physical presence in a large number of countries across the globe, where its Special Collection Service (SCS) inserts eavesdropping devices in difficult-to-reach places. SCS collection tactics allegedly encompass "close surveillance, burglary, wiretapping, and breaking and entering".
Unlike the Defense Intelligence Agency (DIA) and the Central Intelligence Agency (CIA), both of which specialize primarily in foreign human espionage, NSA does not unilaterally conduct human-source intelligence gathering, despite often being portrayed so in popular culture. Instead, NSA is entrusted with assistance to and coordination of SIGINT elements at other government organizations, which are prevented by law from engaging in such activities without the approval of the NSA via the Defense Secretary. As part of these streamlining responsibilities, the agency has a co-located organization called the Central Security Service (CSS), which was created to facilitate cooperation between NSA and other U.S. military cryptanalysis components. Additionally, the NSA Director simultaneously serves as the Commander of the United States Cyber Command and as Chief of the Central Security Service. This ability allowed the government to wiretap the Trump Towers.
We celebrate Easter throughout much of the world based on the death of Jesus Christ and his resurrection after several days with an angel rolling back the stone from his tomb. The logical question is what has this to do with UFOs? I suggest there is a connection. Most of the description of angels compares nicely with our available data of at least one group of the alien visitors that are usually called Nordics. God created angels before humanity, who regularly worship God, and are called the Sons of God. In Genesis, the first book of the Bible and Jewish Torah, angels are described as having bodies with parts such as hands, feet, eyes, head, voices, mouths, hair, faces and other human body parts. The Sons of God or angels are first mentioned in Genesis Chapter 6-2, "That the Sons of God saw the daughters of men that they were fair; and took them wives of which they chose.
The holy angels are powerful spirits present among us, "sent to serve those who are to inherit salvation" (Heb 1:14). "Beside each believer stands an angel as protector and shepherd leading him to life" The angels known as Gods messengers coming from the planet heaven have a key role in numerous religions, and they also seem to be associated with flying vehicles or UFOs. Angels are sometimes referred to as guardians or spirit guides. They are often described as glowing white light emanating from a human form. The ancient Sumerian records describe angels known as cherubim. They were used to protect sensitive areas of the gods and prevent humans from entering. Later angels evolved into more spiritual beings that often were depicted with wings and guarded sacred places such as the tomb where Jesus was buried. They were servants of God and carried out his commands. Angels are usually depicted with wings and the ability to fly, yet descriptions of them in the Bible do not mention wings indicating they likely used some type of unidentified flying object (UFOs) to fly. There are thousands of books written about angels and their appearance to humans.
These angelic visitors are real according to most of the great historic religious writings of mankind and much of Earth's history revolves around these visitors from space who are often identified as Sons of God, gods, or angels. In Vedic writings the topmost authority in the material universe is known as Brahma, and he lives in the highest material planetary system, called Brahmaloka or heaven. It seems reasonable to assume the most sacred writings of our ancestors are not simply myths, but are based on actual visits by angels. Islam believes that the Quran was written in heaven in Arabic and then passed down to Mohammad orally via the angel Gabriel.
Angels are often given credit for bringing writing, agriculture, and religion to Earth. As we celebrate Easter this week, Jesus Christ was crucified, dead, and buried in a tomb for three days and guarded by Roman soldiers. An angel was involved in rolling back the stone of entombment and the rising of Jesus Christ from the dead after his crucifixion. The Resurrection of Jesus is probably the most important part of Christian scripture.
"Early on Sunday morning, as the new day was dawning, Mary Magdalene and other Mary went out to the tomb. Suddenly there was a great earthquake; for an angel of the Lord came down from heaven and rolled aside the stone and sat on it. His face shone like lightning and his clothing was brilliant white. The guards shook with fear when they saw him, and fell into a dead faint. Then the angel spoke to the women, "Don't be frightened!" he said, "I know you are looking for Jesus, who was crucified, but he isn't here! For he has come back to life again, just as he said he would; Come in and see where the body was lying ….. And now, go quickly and tell his disciples that he has risen from the dead, and that he is going to Galilee to meet them there." Mathew 28.
The late Zechariah Sitchin the bestselling author of four books called the "Earth Chronicles" told me that Genesis is true and ought to be read practically as a historic and scientific document. He felt that ancient civilizations are older and much more technically/scientifically oriented than we once believed. Ancient Sumerian texts as well as the Bible reveal the existence of angels or Anunnaki, 'Those who from Heaven to Earth Came.' Only in recent years has modern science caught up with ancient scientific knowledge.
In ancient Mesopotamia, Egypt, and Greece, Judaism, Christianity and in Islam angels were sent as divine messengers to humans to instruct, inform, or command. Many abductees claim healing, love and miracles from the angels. According to the book "Heaven: A History" by Colleen McDannell, ancient Greeks such as Plato and Cicero believed that the soul contained the most vital aspects of the person. Once released from its imprisonment in the body, the angels approached and greeted the soul and invited it to mount a chariot, after testing it for worthiness, and then traveled to a far off place. Hellenistic Jews and later Christians believe in the immorality of the human soul. The soul lived on with angels, and ultimately God in heaven.
"Some have speculated that UFOs could very well be a part of God's angelic host who preside over the physical affairs of universal creation. While we cannot assert such a view with certainty nothing can hide the fact that these unexplained events are occurring with greater frequency around the entire world. Some take the detailed descriptions of a highly credible airline crew and lay them alongside Ezekiel and put forth a strong case —- such theories are now being given serious attention even by people who make no claim to believe in the God of the Bible. UFOs are astonishingly angel-like in some of their reported appearances." Jesus is prophesied to return on a cloud.
David E. Twichell writes, "The ancient astronaut and Biblical UFO hypotheses are not new. However, no one seems to want to take the matter to the next logical step. If Ezekiel's "wheel within a wheel" and Moses' "pillar of fire and cloud" were forerunners of today's UFOs, then the Star of Bethlehem and the brilliant cloud to which Jesus ascended must be treated in the same vein. When Biblical descriptions of anomalous aerial phenomena are overlaid on that of modern-day UFO reports, the picture seems to meld as one. Once the evidence has been presented, the reader is led to a conclusion that is at best convincing and at least thought provoking.
Dan Johnson shot a photo of the moon on April 2, 2017. He states, "I was looking at some more pictures of the moon from Sunday and I saw this one. DSC02381A shows an anomaly between 1 and 2 o'clock near the moon." I have blown this up a bit and DSC02381H1 shows this.
United States 328, Canada 33, United Kingdom 24, Australia 13, Brazil 6, Iran 6, India 5, Mexico 5 The National UFO Reporting Center had 319 sighting reports.
Overgaard — In taking a photo of the full moon over the pine trees of my home on the rim of the mountain tops on April 11, 2017, something flew past me.
It flew fast enough that it was only caught in one frame in the camera view finder, Silent, bright, and oddly hat shaped.
It was gone in the blink of an eye.
Antioch — On March 29, 2017 around 19:05 I saw an object looking like a regular airline passenger jet from San Elijo CT. in Antioch, California at 12 o'clock west and it was hovering in the sky.
After awhile it flew towards the south for some distance and suddenly made a sharp turn and flew north at unbelievable speed.
It flew to the horizon in just 4 to 5 seconds with a sound similar to a drone toy, but it was too far away to hear a drone sound.
Calexico CA – This light formation was photographed the above city heading East on April 10, 2917.
.No sounds just bright yellow lights of two possible triangles.
Woodbury – I was loving the last hours of sun warmth in my yard on March 27, 2017, when I saw a blue green mass moving and hovering in the distance. It was hovering close to twenty minutes and then it just went straight up and was gone.
I took a photo to the northwest as it was very unusual in the clear blue sky. I thought it was a star or a rocket. I felt I had seen an explosion at first but it remained steady in the sky, it shot out of the sky in an instant traveling away and up. I felt oddly fortunate.
Miami Beach — I was on 17th Street and Alton Road on South Beach with my friend in my car on February 12, 2017. At that time I saw this object pass by with low speed and low altitude and made me think it's a police drone. It was too big for regular drone and it had allot of lights with rotating camera on the front.
Rochester — Pamela Stark took this UFO with orb in the pictures on April 10, 2017, at 8:23 PM. My friends were in the sky as usual.
The one was closer to the Earth than ever before and allot brighter.
Apparently aware of the tale, the man's friend quickly took a picture of the creature as it flew away. The astounded men upon seeing their photo the pair became certain that they had seen the notorious cryptic said to lurk in New Jersey's Pine Barrens.
Skeptics will no doubt suggest that the anomaly is merely a bat that looks more monstrous due to the angle and perspective of the photo.
Astoria — I've been watching these things for many months now. During March I've captured blinking orbs spread out in the sky and some weird Smokey-looking stuff. I see these types of things on a regular basis and have plenty of footage to prove it. I live close to a power plant and my theory is they must stay close to it as some sort of power source because they're always lurking around.
Warren — My ex husband and I in the summer of 1985 were driving on Parkman Road and saw a large disk floating above a house. He slapped on the brakes and backed up and we glimpsed the disc as it flew straight upwards and was gone. It happened so fast. It was about 7 PM, on June 15, 1985.
The brushed nickel disk shaped craft had "colors around" as if painted onto the chrome. We also saw lights that shined down but were very dim and shining downwards. It was about the size, or a bit bigger, than a house roof.
Cleveland — April 11, 2017, this UFO was captured on live camera by Youtube user and UFO researcher Orlando Bosca. He caught footage of this UFO while it was moving and we can see that it's huge and not an aircraft.
It must have been seen on radar just from its immense size. UFOs are often seen over water, because they hide below the water until they are ready to carry on with their mission.
Kim Johnson her son and three other friends were driving near the Monroe Institute Saturday night April 1, 2017.and saw a flotilla of 9 UFOs above the tree line. Two were visible and 7 were somewhat cloaked in the clouds. They were cigar shaped and quite long. The witnesses to pictures with their cell phones but pictures didn't show the UFOs like they saw them. Nine cylinder craft or fat cigars were seen flying over the Blue Ridge Mountains in a partial formation. The craft were much larger than commercial aircraft. I will send pictures when others appear. Love and Light, Dan Johnson.
Buenos Aires – On 5 to 6 April 2017 anomalous aerial phenomena were seen above the capital.
Oshawa, Ontario — On April 5, 2017 I was walking the dog in our local park looking to the west over bordering Whitby. I could see a strange white line in the sky some miles away, So I stopped and observed it hovering for a minute. So to capture the moment I took three pictures at 10:33 pm, and being at such a great distance I thought maybe it could be a jet's leftover trail. After a couple minutes I looked again at was gone.
I have discovered two objects next to the St. Peter's Cathedral and took pictures of the scene using my iPhone6.
Two cylinders appear to be flying above the Vatican.
Morelia, Michoacan — This is a dark disk moving slowly over a busy road on March 27, 2017. The person recording the UFO did an outstanding job since they were also driving the car. In the video, we get to see its smooth movement and its disk edge.
The UFO was captured in broad daylight. This strange black object was circular transiting through the release at the height of the Technological of Morelia. It moves gracefully and silently over the city. It's rare to see such a close up of a real UFO.
La Molina LIMA — My brother woke me up as he noticed it first, then me, my brother, and my dad all watched it. I filmed it on my cell phone, and my dad also filmed it on his cell phone.
We all knew it was something strange, and it must be a UFO. The object just hovered in place and pulsated light. It disintegrated twice and returned. It would just disappear for half a second and then reappear. It appeared to be vibrating and pulsating. It would disappear and reappear in a separate location just a little to the left .It would appear to move to the left. I didn't feel any emotion besides the amazement of the event.
No emotion or feelings other than being amazed.
I didn't see it disappear, suddenly it wasn't there anymore.


text:
Q: Get notified when instance of app widget was added to homescreen by user I've developed an android app where you can customize your own widget.
My problem: I want to start an Intent when an instance of the widget is added to the homescreen.
Until now I'm using the onReceive() method, which is called by the system in my AppWidgetProvider class as soon as anything happens. There I wait for an Intent which is sent by the system and which has the intent action
    AppWidgetManager.ACTION_APPWIDGET_OPTIONS_CHANGED
(onWidgetDataOptionsChanged() is according to android developer page the right method: http://developer.android.com/guide/topics/appwidgets/index.html).
I've tried this on several real devices and it always works. But on all emulators with new android versions, no Intent with the above mentioned action is sent by the system when I add a widget instance to my homescreen.
So, is the issue caused by the emulators or should I wait for another kind of action?

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The name of the small village pays tribute to the Risorgimento statesman who was the mayor of Grinzane for seventeen years. The town is divided into two centers- Gallo and Grinzane Cavour.
Gallo is home to numerous artisan and industrial activities while the center of the town consists of Grinzane Cavour, with its few houses, narrow around the austere silhouette of the castle built in the first half of the thirteenth century.
Today, the castle of Grinzane Cavour houses the Enoteca Regionale Piemontese Cavour and an ethnographic museum on rural life. Since 1983 the castle is also home to the Grinzane Cavour Literary Prize for Italian and foreign fiction, created by the Turin International Publishing company to promote the dissemination of literature among young people. The second Sunday of November the castle hosts the famous International White Truffle of Alba Auction.


Seth Rogen has had enough with white supremacists on Twitter.
This week, the actor took to the platform to tell followers that he has been sending direct messages to the site's CEO, Jack Dorsey, about the number of white supremacist accounts that the site has granted the verified blue checkmark. Rogen didn't give any more details on the conversation, but Twitter has come under fire many times in the past for lending a platform to white supremacists.
As Mediaite noted last year, the social media platform faced a backlash for verifying the account of Jason Kessler, the organizers of the racially charged demonstration in Charlottesville, Virginia, that turned deadly. Kessler also used Twitter to attack Heather Heyer, the counter-protester who was killed when one of the white supremacists allegedly drove his car through a crowd of people.
Kessler said Heyer was a "fat, disgusting communist" and claimed that her death was "payback time" for people who had been killed by communist regimes.
Twitter has gone on periodic "purges" where large swaths of white supremacists have had their accounts deleted, but Newsweek noted that these left many of the loudest and most notorious elements of the movement.
A number of organizations and individuals have put pressure on Twitter to stop allowing this speech on its platform, though the site has been reluctant to make any large-scale changes. The site also offered no response to Rogen's tweet calling for changes.
Seth Rogen has never been shy about speaking out against the far-right. Late last year, he refused to make an appearance on SiriusXM because of the platform's support of Steve Bannon, the controversial former adviser to Donald Trump. Rogen also recently bragged that he turned down House Speaker Paul Ryan for a photograph.