<p>As you are handling with time series data and you want to find trends; A good approach should be consider applying <a href="https://otexts.com/fpp2/holt-winters.html" rel="nofollow noreferrer">Holt-Winter's seasonal method</a>. This algorithm handle seasonal, trend and smooth parameters. A good implementation of this kind of algorithm is <a href="https://facebook.github.io/prophet/docs/quick_start.html#r-api" rel="nofollow noreferrer">Prophet by Facebook</a>. You can code an exploratory analysis with this library and obtain trend, yearly seasonality, and weekly seasonality of the time series, among other components. Example:</p>

<p><a href="https://i.sstatic.net/pDyOB.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/pDyOB.png" alt="enter image description here"></a></p>

CC BY-SA 4.0
2019-08-30T18:23:24.510Z
2019-08-30T18:23:24.510Z
Bitzel
<p>I have a very big table with lots of names and how much they are searched by date. </p>

<p>I would like to find trending patterns. When does a name rise and when does it fall. Without knowing the name or the pattern before.
The rise could be during the seasons of the year but also during a week.</p>

<p>Like a 'warm hat' is trending in winter and falling in summer.
Or searches for a "board game" might rise on Sunday and decrease on Monday.</p>

<p>The table looks simplified like this:</p>

<pre><code>winter gloves, 2014-01-01, 200
warm hat, 2014-01-01, 300
swimming short, 2014-01-01, 1
sunscreen, 2014-01-01, 2
....
winter gloves, 2014-07-01, 1
warm hat, 2014-07-01, 1
swimming short, 2014-07-01, 200
sunscreen, 2014-07-01, 300
</code></pre>

<p>Which algorithms should I have a look at? </p>

<p>Thanks for any hint,
Joerg</p>

<data-mining>
Algorithm for seasonal trends
JoergP
# Algorithm for seasonal trends

(Asked by: username_0 on 2019-08-29)

I have a very big table with lots of names and how much they are searched by date. 
I would like to find trending patterns. When does a name rise and when does it fall. Without knowing the name or the pattern before.
The rise could be during the seasons of the year but also during a week.
Like a 'warm hat' is trending in winter and falling in summer.
Or searches for a "board game" might rise on Sunday and decrease on Monday.
The table looks simplified like this:
winter gloves, 2014-01-01, 200
warm hat, 2014-01-01, 300
swimming short, 2014-01-01, 1
sunscreen, 2014-01-01, 2
....
winter gloves, 2014-07-01, 1
warm hat, 2014-07-01, 1
swimming short, 2014-07-01, 200
sunscreen, 2014-07-01, 300

Which algorithms should I have a look at? 
Thanks for any hint,
Joerg

**Tags:** data-mining


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## 1 answers

### Answer by username_1 on 2019-08-30. Score: 1

As you are handling with time series data and you want to find trends; A good approach should be consider applying [Holt-Winter's seasonal method](https://otexts.com/fpp2/holt-winters.html). This algorithm handle seasonal, trend and smooth parameters. A good implementation of this kind of algorithm is [Prophet by Facebook](https://facebook.github.io/prophet/docs/quick_start.html#r-api). You can code an exploratory analysis with this library and obtain trend, yearly seasonality, and weekly seasonality of the time series, among other components. Example:

[![enter image description here](https://i.sstatic.net/pDyOB.png)](https://i.sstatic.net/pDyOB.png)


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