Explain Holt winters in brief.
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Holt’s winter method does exponential smoothing.
In simple moving average, the past observations are weighted equally,
while in exponential smoothing (fancy term for saying that more recent historical data
adds more value to results in forecasting than than older data), the exponential functions
are applied to assign exponentially decreasing weights over time. so, the most recent observations
are given more weightage than the previous ones.
First lets understand 3 types of smoothing
1) Simple exponential smoothing – Puts weight on more recent data to smooth out data
But, this method does not work if there is seasonality and trend in data.
2) Holt’s exponential smoothing – allows for data with a trend.
3) winter’s exponential smoothing – adds seasonality into the model
4) Holt-winter’s method – allows for data with a trend and seasonal cycle.
The Holt-winter’s additive method is useful when the seasonal variation is constant.
The Holt-winter’s multiplicative method is useful when the seasonal variation
changes in proportion to the level of the time series.
code:
HoltWinters(rainseries, beta=FALSE, gamma=FALSE,)
In the above code snippet
rainseries -> timeseries data
beta –> if set to False, the function will do exponential smoothing
gamma –> for seasonal component, if set to False a non-seasonal component is fitted
Well explained 🙂
Consider a hotel located on a hill station. It experiences high visits during the summer season whereas the visitors during the rest of the year are comparatively very less. Hence the profit earned by the owner will be far better in summer season than in any other season. This pattern will repeat itself every year. Such a repetition is called Seasonality.
Hence we need a method that takes into account both trend and seasonality to forecast future prices. One such algorithm that we can use in such a scenario is Holt’s Winter method. The idea behind triple exponential smoothing(Holt’s Winter) is to apply exponential smoothing to the seasonal components in addition to level and trend.
The Holt-Winters seasonal method comprises the forecast equation and three smoothing equations which can be seen in the image.
The level equation shows a weighted average between the seasonally adjusted observation and the non-seasonal forecast for time t. The trend equation is identical to Holt’s linear method. The seasonal equation shows a weighted average between the current seasonal index, and the seasonal index of the same season last year (i.e., s time periods ago).
In this method also, we can implement both additive and multiplicative technique. 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.
HoltWinters(data, beta=FALSE, gamma=FALSE)
Holt Winters Method Equations.