When it comes to time series forecasting, I’m a great believer that the simpler the model, the better.

However, not all time series are created equal. Some time series have a strongly defined trend — we often see this with economic data, for instance:

Source: Federal Reserve Economic Data

Others show a more stationary-like pattern — e.g. monthly air passenger numbers:

Source: San Francisco Open Data

The choice of time series model will depend highly on the type of time series one is working with. Here are some of the most useful time series models I’ve encountered.

1. ARIMA

In my experience, ARIMA tends to be most useful when modelling time series with a strong trend. The model is also adept at modelling seasonality patterns.

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