The Time Series Forecasting with Python training takes 1 day. 

The training starts with defining the time series forecasting problem. Then, we will talk about stationarity, a property that is prerequisite for some models, and learn how to test for it. We will then discuss transformations necessary to do before modelling and ACF, PACF plots to help decide what model to use. We will also learn about the STL decomposition, ETS, ARIMA, STL-ARIMA forecasting models and how we can evaluate our models. Finally, we will talk about new models such as Prophet and tRNNs and tLSTMs.

After this, we do a hands-on lab session, where we practice all of the learned concepts in Python, using real world datasets. The training includes theory and hands-on exercises. After this training you will have gained knowledge about:

• Stationarity and statistical tests for it
• STL decomposition
• Time series transformations and differencing
• Autocorrelation (ACF) and Partial Autocorrelation function (PACF)
• ETS model
• ARIMA model
• STL-ARIMA model
• In-sample evaluation methods: AIC, AICc and BIC
• Diagnostics of the model residual
• Out-of-sample evaluation metrics: MAE, RMSE, MAPE, MASE
• Time series cross-validation
• Prophet
• tRNNs and tLSTMs