Which is better Holt-Winters or ARIMA?

Even with very little difference, the Holt-Winters additive model showed the best results for forecasting rice prices compared to the ARIMA model. Thus, both models can be used to forecast the prices of agricultural products.

What is Holt-Winters method?

The Holt-Winters method uses exponential smoothing to encode lots of values from the past and use them to predict “typical” values for the present and future. Exponential smoothing refers to the use of an exponentially weighted moving average (EWMA) to “smooth” a time series.

Is exponential smoothing better than ARIMA?

I found the only difference between ARIMA and Exponential smoothing model is the weight assignment procedure to its past lag values and error term. In that case Exponential should be considered much better that ARIMA due to its weight assigning method.

Why is ARIMA better than ETS?

The ARIMA model outperforms the ETS model on bias, but it’s very close. This is also visible in how similar the forecast plots look. However, when comparing how the test set performs, the ARIMA model outperforms the ETS model by a greater margin, and therefore is the best model for this solar consumption data.

What is exponential triple smoothing?

Triple exponential smoothing is used to handle the time series data containing a seasonal component. This method is based on three smoothing equations: stationary component, trend, and seasonal. Both seasonal and trend can be additive or multiplicative.

What are the criteria we use to compare ARIMA models?

The best ARIMA model have been selected by using the criteria such as AIC, AICc, SIC, AME, RMSE and MAPE etc. To select the best ARIMA model the data split into two periods, viz. estimation period and validation period. The model for which the values of criteria are smallest is considered as the best model.

What is the difference between Holt-Winters additive and multiplicative?

The additive method is preferred when the seasonal variations are roughly constant through the series, while the multiplicative method is preferred when the seasonal variations are changing proportional to the level of the series.

Why are Holt-Winters algorithms popular?

The Holt-Winters forecasting algorithm allows users to smooth a time series and use that data to forecast areas of interest. Exponential smoothing assigns exponentially decreasing weights and values against historical data to decrease the value of the weight for the older data.

Which is the equivalent Arima model for Holt’s exponential smoothing method?

0,1,1
An equivalent ARIMA(0,1,1) model can be constructed to represent the single exponential smoother. Double exponential smoothing (also called Holt’s method) smoothes the data when a trend is present.

Is ETS exponential smoothing?

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).

What is Tbats model?

The TBATS model is a time series model for series exhibiting multiple complex seasonalities. The TBATS model was introduced by De Livera, Hyndman & Snyder (2011, JASA). “TBATS” is an acronym denoting its salient features: T for trigonometric regressors to model multiple-seasonalities. B for Box-Cox transformations.

What is level in Holt-Winters?

The level (alpha) parameter must be larger than 0 but not larger than 1. A small value means that older values in the X direction are weighted more heavily. Values near 1.0 mean that the latest value has more weight. Leave the field blank to let the Holt-Winters function automatically find the optimal value of alpha.