Abstract: Aspects if the disclosure are directed towards multi-step forecasting via temporal aggregation. An example embodiment includes a method the includes receiving a time series including a first time step value and a second time step value. The method can further include generating a temporally aggregated time series by summing the first time step value and the second time step value to create a third time step value. The method can further include calculating a first set of input values and a second set of input values from the temporally aggregated time series. The method can further include forecasting a fourth time step value using the first set of input values and the second set of input values, and a fifth time step using the second set of input values from the temporally aggregated time series.

Abstract: Techniques are described for providing explanation information for time series-based predictions made using statistical models, such as linear statistical models, examples of which include various Exponential Smoothing models, Autoregressive Integrated Moving Average (ARIMA) models, and others. For a forecast predicted by a statistical model that has been trained upon and/or fit to a set of historical times series data points, an explanation is generated for the forecast, where the explanation for the forecast includes information indicative of the importance or impact or influence of individual time series data points in the set on the forecast. The explanation for the forecast may be output to a user along with the forecast. This enables the user to have some visibility into why the particular forecast was predicted by the statistical model.