Application of ETS model in machine learning

The ETS model is a time series model, including a horizontal component, a trend component (T), a seasonal component (S) and an error term (E). Together, these components form the underlying state space of the model.

Characteristics of ETS model

1. It is not fixed or static.

2. Use exponential smoothing

3. If the data has a trend and/or seasonality, you can use this model because it explicitly Model these components.

The ETS model, which stands for Error-Trend-Seasonality, is a time series decomposition model. It divides the series into three parts: error, trend and seasonality. When dealing with time series data, it is a univariate forecasting model. It focuses on seasonal and trending elements. Trend Technical Model, Exponential Smoothing and ETS Decomposition are some of the principles included in this model.

Using the three important variables of error, trend, and seasonality helps create a model that fits your data. These terms will be used for "smoothing" in the ETS model.

ETS models are very useful for understanding trends and seasonality in time series data.

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