forecasting

A while ago I created a notebook with an introduction to time series analysis. Here is this notebook as a Gist: Generate a synthetic time series with cycles, trend (random walk) and noise components Look at some descriptive statistics (e.g. autocorrelations) Model the synthetic data with a SARIMA model Working with synthetic data first forces you to be explicit about your assumptions and is great for debugging: Unlike real data, as you know the true process the synthetic data follows you can validate your estimates easily against the “true” values....