1 Understanding time series forecasting

Time series exist in a variety of fields from meteorology to finance, econometrics, and marketing. By recording data and analyzing it, we can study time series to analyze industrial processes or track business metrics, such as sales or engagement. Also, with large amounts of data available, data scientists can apply their expertise to techniques for time series forecasting.

1.1 Introducing time series

1.1.1 Components of a time series

1.2 Bird’s-eye view of time series forecasting

1.2.1 Setting a goal

1.2.2 Determining what must be forecast to achieve your goal

1.2.3 Setting the horizon of the forecast

1.2.4 Gathering the data

1.2.5 Developing a forecasting model

1.2.6 Deploying to production

1.2.7 Monitoring

1.2.8 Collecting new data

1.3 How time series forecasting is different from other regression tasks

1.3.1 Time series have an order

1.3.2 Time series sometimes do not have features

1.4 Next steps

Summary