The ultimate course to master time series forecasting in Python
By the end of this course, you will be able to tackle any forecasting problem with confidence and have a portfolio of 16 forecasting projects!
In this 11 hour course, you will:
- Define time series data
- Learn when forecasting not applicable (random walk model)
- Forecast with the SARIMAX family of models (model seasonal data and add exogenous variables)
- Forecast with exponential smoothing
- Perform multivariate forecasting
- Learn to deal with multiple seasonal periods (using BATS and TBATS)
- Forecast using decomposition (Theta model)
- Develop a framework to apply deep learning models like DNNs, LSTMs, and CNNs
- Work with the automated forecasting library Prophet
- Apply state-of-the-art forecasting models:
- N-BEATS
- N-HiTS
- PatchTST
- TimesNet
- TimeGPT
- and more to come in future updates
- Predict sparse time series
Starter notebooks are available to follow easily along and all solutions are provided on GitHub!