Exponential Smoothing (ETS) Algorithm - Amazon Forecast

Exponential Smoothing (ETS) Algorithm

Exponential Smoothing (ETS) is a commonly-used local statistical algorithm for time-series forecasting. The Amazon Forecast ETS algorithm calls the ets function in the Package 'forecast' of the Comprehensive R Archive Network (CRAN).

How ETS Works

The ETS algorithm is especially useful for datasets with seasonality and other prior assumptions about the data. ETS computes a weighted average over all observations in the input time series dataset as its prediction. The weights are exponentially decreasing over time, rather than the constant weights in simple moving average methods. The weights are dependent on a constant parameter, which is known as the smoothing parameter.

ETS Hyperparameters and Tuning

For information about ETS hyperparameters and tuning, see the ets function documentation in the Package 'forecast' of CRAN.

Amazon Forecast converts the DataFrequency parameter specified in the CreateDataset operation to the frequency parameter of the R ts function using the following table:

DataFrequency (string) R ts frequency (integer)
Y 1
M 12
W 52
D 7
H 24
30min 2
15min 4
10min 6
5min 12
1min 60

Supported data frequencies that aren't in the table default to a ts frequency of 1.