Forecasting large collections of time series

With the recent launch of Amazon Forecast, I can no longer procrastinate writing about forecasting “at scale”! Quantitative forecasting of time series has been used (and taught) for decades, with applications in many areas of business such as demand forecasting, sales forecasting, and financial forecasting. The types of methods taught in forecasting courses tends to be discipline-specific: Statisticians love ARIMA (auto regressive integrated moving average) models, with multivariate versions such as Vector ARIMA, as well as state space models and non-parametric methods such as STL decompositions. Econometricians and finance academics go one step further into ARIMA variations such as ARFIMA (f=fractional), … Continue reading Forecasting large collections of time series