Is Holt-Winters Exponential smoothing?
Holt-Winters uses exponential smoothing to encode lots of values from the past and use them to predict “typical” values for the present and future. If you’re not familiar with exponential smoothing, we wrote a previous post about it.
Is ARIMA better than Exponential smoothing?
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.
Which is easier to use, ARIMA or Holt-Winters?
Holt-Winters has three parameters, so it’s simple, but they’re basically smoothing factors so it doesn’t tell you much if you know them. ARIMA has more parameters, and some of them have some intuitive meaning, but it still doesn’t tell you much. State Space can be complex, but you can also explicitly model things for greater explanatory power.
Is the Holt-Winters model the same as the Sarima model?
There is a model underlying Holt-Winters (HW) which is close – not identical – to the simple SARIMA (0,1,1) (0,1,1) s model ( s denoting the seasonal period), see e.g. the Broze-Mélard paper on ResearchGate. That simple model can fit a large proportion of series, hence the success of HW. Of course, everything depends on the data. 2.
How to use Holt-Winters out of the box?
Second, you can use Holt-Winters out of the box with InfluxDB. Finally, the InfluxData community has requested an explanation of Holt-Winters in this Github issue 459. Luckily for us, Holt-Winters is fairly simple, and applying it with InfluxDB is even easier. When to use Holt-Winters. How Single-Exponential Smoothing works.
How does Holt-Winters forecast differ from other models?
Time for model evaluation. Visual inspection of the forecasting figures above does not show much difference in forecasting results, and it’s probably because of predictable seasonality and trend components. However, Holt-Winters does show (at least visually) a bit of higher uncertainty around the forecast values.