These notebooks are supplement material for the blog here: Overview of Time Series Forecasting from Statistical to Recent ML Approaches
Topics and corresponding notebooks:
- Introduction to TS (Time_Series_Intro_ES_ARIMA.ipynb)
- Decompose (Time_Series_FFT.ipynb)
- Gen Synthic
- Decompose FFT
- Naive approaches (Time_Series_Intro_ES_ARIMA.ipynb.ipynb)
- Statistical (Time_Series_Intro_ES_ARIMA.ipynb.ipynb)
- Smoothing techniques (MA, ES)
- ARIMA
- State Space (Time_Series_StateSpace.ipynb)
- ML (Time_Series_ML-LR_XGBoost.ipynb)
- Linear Regression
- Decision Tree (XGBoost)
- DL (Time_Series_DL_LSTM_CNN.ipynb)
- LSTM, CNN + LSTM
- TCN (Time_Series_DL_TCN_LSTNet.ipynb)
- LSTNet
- TFT (Time_Series_DL_TFT_N-BEATS.ipynb)
- N-BEATS
- Commercial: (Time_Series_FBProphet_DeepAR.ipynb)
- Facebook Prophet
- Amazon DeepAR