Peramalan dengan Menggunakan Metode Holt-Winters Exponential Smoothing | Semantic Scholar (2024)

FAQs

What is the difference between exponential smoothing and Holt-winters? ›

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.

Holt-Winters is a method that aligns with only one of the models from the framework. Now, if you have data without a trend, we know that the trend-based models will perform poorly, overfitting the data. Similarly, with seasonal models on non-seasonal data or other misalignments between the model and the data.

Holt's Smoothing method: Holt's smoothing technique, also known as linear exponential smoothing, is a widely known smoothing model for forecasting data that has a trend. Winter's Smoothing method: Winter's smoothing technique allows us to include seasonality while making the prediction along with the trend.

Exponential smoothing is the weighted average of the past data, with the recent data points given more weight than earlier data points. The weights decay exponentially towards the earlier data points, hence, the name.

The Holt-Winters method has several advantages and disadvantages. One advantage is that it is a recursive model, which means it can capture trends and seasonality in time series data ⁴. Another advantage is that it can handle missing data by using modified Holt-Winters recursive formulas ¹.

Winter's method assumes that the time series has a level, trend and seasonal component. A forecast with Winter's exponential smoothing can be expressed as: The forecast equation is the extenuation of both the SES and HES methods, finally augmented with the inclusion of the Seasonal, S, component.

Holt-Winters' damped method

A method that often provides accurate and robust forecasts for seasonal data is the Holt-Winters method with a damped trend and multiplicative seasonality: ^yt+h|t=[ℓt+(ϕ+ϕ2+⋯+ϕh)bt]st+h−m(k+1)ℓt=α(yt/st−m)+(1−α)(ℓt−1+ϕbt−1)bt=β∗(ℓt−ℓt−1)+(1−β∗)ϕbt−1st=γyt(ℓt−1+ϕbt−1)+(1−γ)st−m.

Exponential smoothing is one of the oldest and most studied time series forecasting methods. It is most effective when the values of the time series follow a gradual trend and display seasonal behavior in which the values follow a repeated cyclical pattern over a given number of time steps.

What would be the predominant reason why you would use Holt's exponential smoothing forecast method? It is simple to calculate by hand. It works best for data that is stationary. It is useful for data that has a trend, but no seasonality.

How do you use exponential smoothing model? ›

The exponential smoothing calculation is as follows: The most recent period's demand multiplied by the smoothing factor. The most recent period's forecast multiplied by (one minus the smoothing factor). S = the smoothing factor represented in decimal form (so 35% would be represented as 0.35).

However, it does not consider the most recent inter-trend relations, which can be a disadvantage. The advantages of using Holt's method with different time horizons include accurate forecasting, while the disadvantages include increased complexity and potential for errors.

Another benefit is that spikes in the data aren't quite as detrimental to the forecast. In exponential smoothing, the most recent forecast has the greatest weight and therefore should be the most accurate in predicting demand, as opposed to the moving averages method where the weight for each period is fixed.

Exponential smoothing

It uses weighted averages with the assumption that past trends and events will mirror the future. When compared to other quantitative methods, it makes it easier to come up with data-driven predictions without the need to analyze multiple data sets.

Exponential Smoothing Methods combine Error, Trend, and Seasonal components in a smoothing calculation. Each term can be combined either additively, multiplicatively, or be left out of the model. These three terms (Error, Trend, and Season) are referred to as ETS.

The difference between simple and exponential smoothing of data is related to the weight that is applied on the observations. In simple smoothing, a uniform weight is allotted to all the observations, while in exponential smoothing higher weight is placed on the most recent observations.

Question: Winters' model differs from Holt s model and simple exponential smoothing in that it includes an index for seasonality residuals cyclical fluctuations trend. There's just one step to solve this.

In exponential smoothing, the most recent forecast has the greatest weight and therefore should be the most accurate in predicting demand, as opposed to the moving averages method where the weight for each period is fixed.