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

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@article{Aryati2021PeramalanDM, title={Peramalan dengan Menggunakan Metode Holt-Winters Exponential Smoothing}, author={Ayu Aryati and Ika Oktaviana Purnamasari and Yuki Novia Nasution}, journal={EKSPONENSIAL}, year={2021}, url={https://api.semanticscholar.org/CorpusID:271056466}}
  • Ayu Aryati, I. Purnamasari, Y. N. Nasution
  • Published in EKSPONENSIAL 19 January 2021
  • Economics, Business
  • EKSPONENSIAL

Forecasting is a technique for estimating a value in the future by looking at past and current data. Foreign tourists are everyone who visits a country outside their place of residence, driven by one or several needs without intending to earn income in the place visited and the duration of the visit is no more than twelve months. The method used in this study is the Holt-Winters smoothing smoothing method. In this study used data of foreign tourists visiting Indonesia in January 2014…

12 Citations

Background Citations

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The calculation results with Single Exponential Smnothing have the lowest MAPE value, namely 1.41005% with a value of α = 0.1, the number of new student admissions is 664 people.

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Volume impor produk migas dan non migas Indonesia periode September tahun 2022 ialah sebesar 15.369,00 ribu ton, dalam hal ini impor migas dan non migas Indonesia mengalami peningkatan dari tahun

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    Regi Muzio Ponziani

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    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.

    What are the limitations of Holt Winter's method? ›

    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.

    What does Holt-Winters exponential smoothing procedure allow for? ›

    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.

    What is exponential smoothing in research methodology? ›

    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.

    What are the three types of exponential smoothing? ›

    The three types of exponential smoothing are:
    • Simple or single exponential smoothing.
    • Double exponential smoothing.
    • Triple exponential smoothing.
    Aug 16, 2023

    What are the advantages of Holt Winters method? ›

    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.

    What are the assumptions of Holt-Winters model? ›

    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.

    What is the Holt-Winters formula? ›

    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.

    When should you use exponential smoothing? ›

    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? ›

    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).

    What are the disadvantages of Holt Winter's method? ›

    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.

    What are the benefits of exponential smoothing? ›

    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.

    Is exponential smoothing Qualitative or quantitative? ›

    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.

    What is the difference between exponential smoothing and ETS? ›

    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.

    What is the difference between exponential smoothing and simple exponential smoothing? ›

    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.

    How winters model differs from simple exponential smoothing in that it does include a term for? ›

    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.

    Why is exponential smoothing more accurate? ›

    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.

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