Module 2 -Part 2- Setting up Deep Forecasting environment, basic Python timeseries

Relevant playlists:
Deep Forecasting Concepts, simply explained: https://www.youtube.com/playlist?list=PL2GWo47BFyUPW_lptTNwpKNrpEQvUZerR
Machine Learning Codes and Concepts: https://youtube.com/playlist?list=PL2GWo47BFyUNeLIH127rVovSqKFm1rk07&si=lCPyHenEQYBCJzQ_
Deep Learning Concepts, simply explained: https://www.youtube.com/playlist?list=PL2GWo47BFyUO6Fiy2mJCxR8sUrBEfT6BM
Instructor: Pedram Jahangiry

All of the slides and notebooks used in this series are available on my GitHub page, so you can follow along and experiment with the code on your own.
https://github.com/PJalgotrader

Lecture Outline:
0:00 recap and where to find the materials!
1:30 Running the notebook in Google Colab
3:56 Running the notebook on VsCode locally (creating a conda environment for the course)
13:38 Importing data, fixing the time index, visualization
27:55 Data transformation (log, power, boxcox)
39:17 ACF and PACF plots
41:23 Stationarity and differencing
51:54 Seasonal decomposition
57:49 Creating forecasting benchmarks (naiva, seasona naive, mean and drift method)

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