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Exponential Smoothing (ETS)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'
Amazon Forecast converts the DataFrequency parameter specified in the CreateDataset operation to the
frequency parameter of the R ts
| 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.
