How to learn time series in 5 minutes: P1-Univariate single step out time series prediction

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Q: Why time series?
A: Many practical prediction problems have time component and the seasonality inside these dates has valuable information that cannot be neglected. Time series problems can be categorized into 4 groups,
1- Univariate (one feature to use in training) and single step (predicting just one point in the future)
2- Multivariate (multiple features to use in training) and single step (predicting just one point in the future)
3- Univariate (one feature to use in training) and multi-step (predicting multiple points in the future)
4- Multivariate (multiple features to use in training) and multi-step (predicting multiple points in the future)
In this video I first explain the formulation of time series and then I write a minimal code to show how a univariate and single step time series can be predicted in python.

Code:

🕒 VIDEO SECTIONS 🕒
0:00 - 4 types of time series
0:49 - Univariate single step time series
1:09 - Creating X and Y from time series
4:04 - LSTM model training
4:52 - Single step out prediction

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Thank you so much. This was really helpful👌

neginpirannanekaran

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