time series analysis

What’s ARIMA and Why Should You Care? If you’re working with time series and you need to produce forecasts, autoregressive moving-average models (AR(I)MA) are still a good place to start. But which Python implementation should you use (if you don’t want to use R)? Recently I’ve been again looking into what the Python ecosystem has to offer in regards to time series analysis in general and ARIMA models in particular. There are quite a few options, however you should have a rough understanding what’s happening under the hood: Are we dealing with a framework that wraps existing libraries or native implementations?...

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....