<p>welcome to the community.</p>
<p>Let me imagine this problem to be a classification task depending upon the smartwatch accelerometer and gyroscope data collected over time (time series);</p>
<p>Say we have a target variable where we need to predict what activity the human is doing;</p>
<p>You can proceed to input these numeric data normally in your algorithm (either a DNN or some ML algorithm) and let the algorithm figure out what the target variable would be. Don't forget to standardize your features!</p>
<p>To proceed with these kinds of problems, I always suggest doing some EDA, i.e. checking how these 7 features vary depending on the target class. For example, on the smartphone data (accelerometer) for an idle person (sitting or lying)  is drastically different from a dynamic person (walking, running).</p>
<p>If you see these patterns already, you can expect your algorithm to perform reasonably well. Good luck :)</p>

CC BY-SA 4.0
2024-08-30T06:58:20.390Z
2024-08-30T06:58:20.390Z
Suvo
<p>I have multiple independent simulated time series data (for physic experiment). Each time series has t = 0 to 10000, and 7 features with 1 target. I want to build a model that I can train on all the simulated data and make prediction on dataset Test which has only 7 features and the Time column (t=0 to 10000) without the Target. (Completely independent case of the training set)</p>
<p>I tried to think this as a time-series problem since target is time dependent but my goal here seems not? Because if I train a time series model, my Test set needs a historical part, but in my case,my data does not.</p>

<time-series>
How should I see this problem as? Normal or Time Series?
Khoa Tan Dang
# How should I see this problem as? Normal or Time Series?

(Asked by: username_0 on 2024-08-29)

I have multiple independent simulated time series data (for physic experiment). Each time series has t = 0 to 10000, and 7 features with 1 target. I want to build a model that I can train on all the simulated data and make prediction on dataset Test which has only 7 features and the Time column (t=0 to 10000) without the Target. (Completely independent case of the training set)

I tried to think this as a time-series problem since target is time dependent but my goal here seems not? Because if I train a time series model, my Test set needs a historical part, but in my case,my data does not.

**Tags:** time-series


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## 1 answers

### Answer by username_1 on 2024-08-30. Score: 1

welcome to the community.

Let me imagine this problem to be a classification task depending upon the smartwatch accelerometer and gyroscope data collected over time (time series);

Say we have a target variable where we need to predict what activity the human is doing;

You can proceed to input these numeric data normally in your algorithm (either a DNN or some ML algorithm) and let the algorithm figure out what the target variable would be. Don't forget to standardize your features!

To proceed with these kinds of problems, I always suggest doing some EDA, i.e. checking how these 7 features vary depending on the target class. For example, on the smartphone data (accelerometer) for an idle person (sitting or lying) is drastically different from a dynamic person (walking, running).

If you see these patterns already, you can expect your algorithm to perform reasonably well. Good luck :)


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