How can you use exponential smoothing to forecast future demand?

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Forecasting future demand is a crucial task for any marketer who wants to optimize their budget, inventory, and strategy. But how can you account for the fluctuations and trends in your historical data? One possible solution is exponential smoothing, a technique that assigns different weights to past observations and produces a smoothed estimate of the future. In this article, you will learn what exponential smoothing is, how it works, and how you can apply it to your own data.

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Daniel Pereira

Itamaracá CSPRNG | Victoria | HHR, HCR, and HDR Metrics | FRSS

1 What is exponential smoothing?

Exponential smoothing is a method of time series analysis that uses a weighted average of past values to predict the future. The idea is that more recent data points are more relevant and reliable than older ones, so they should have more influence on the forecast. The weights are calculated using a smoothing parameter, which determines how fast the weights decay over time. The higher the smoothing parameter, the more responsive the forecast is to the latest data, but also the more volatile and prone to overfitting. The lower the smoothing parameter, the more stable and smooth the forecast is, but also the more lagging and insensitive to changes.

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Nicholas Cappon

Building great apps to solve business problems.
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Exponential smoothing is a time series forecasting technique that can be effective when trying to predict outcomes for data that contain seasonal trends over days, weeks, months, quarters or years. This method works by giving exponentially decreasing weights to past observations. This allows the statistical model to give a higher level of importance to more recent observations and smooth out the data fluctuations over time. Exponential smoothing models can be validated through metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) and the parameters can be adjusted to tune the model to improve accuracy.

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2 How does exponential smoothing work?

Exponential smoothing works by applying a formula that updates the forecast based on the previous forecast and the previous observation. The formula is: Forecast(t+1) = Smoothing parameter * Observation(t) + (1 - Smoothing parameter) * Forecast(t) As you can see, the forecast for the next period is a combination of the actual value for the current period and the forecast for the current period, weighted by the smoothing parameter. The smoothing parameter can range from 0 to 1, and it reflects how much weight is given to the most recent observation. For example, if the smoothing parameter is 0.9, then 90% of the weight is given to the current observation, and only 10% to the previous forecast. If the smoothing parameter is 0.1, then 10% of the weight is given to the current observation, and 90% to the previous forecast.

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3 How can you choose the smoothing parameter?

The smoothing parameter is a key factor that affects the accuracy and performance of exponential smoothing. However, there is no definitive rule or formula to determine the optimal value for it. Instead, you have to experiment with different values and compare the results using some criteria, such as the mean absolute error (MAE), the mean squared error (MSE), or the root mean squared error (RMSE). These are measures of how close the forecast is to the actual data, and the lower they are, the better. You can also use a technique called cross-validation, which splits the data into training and testing sets, and evaluates the forecast on the testing set using different values of the smoothing parameter.

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Daniel Pereira

Itamaracá CSPRNG | Victoria | HHR, HCR, and HDR Metrics | FRSS
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In fact, one of the permanent challenges in exponential smoothing formulas is that there is no formula that will always tell us the "optimal" value of the constants α, β and γ (if we consider the Holt-Winters Model, for example). This being the case, the definition of the ''optimum'' value is done through trial and error as well as experience. However, depending on the characteristics of the Time Series and the application of the same methods continuously, the researcher tends to become very familiar with this scenario and have the values close to the optimal constants in mind without having to test too much.

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4 How can you apply exponential smoothing to your data?

To apply exponential smoothing to your data, you need a time series of historical observations, such as sales, revenue, or web traffic. There are many software and tools available to perform the calculations and generate the forecast, such as Excel, R, Python, or online calculators. The basic steps include selecting a value for the smoothing parameter (or using the default value of 0.5), initializing the forecast for the first period (by using either the first observation or the average of the first few observations), applying the exponential smoothing formula to update the forecast for each subsequent period (using both the previous forecast and previous observation), evaluating the forecast with error measures or cross-validation technique (and adjusting the smoothing parameter if needed), and plotting the forecast and actual data to visualize the results and identify any patterns or anomalies.

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5 What are the advantages and limitations of exponential smoothing?

Exponential smoothing has some advantages and limitations that you should be aware of before using it. It is simple to implement and understand, requires minimal data and parameters, and does not assume any specific distribution or trend. Additionally, it can adapt to changes in the data, handle missing values and outliers, and smooth out irregularities and noise. However, exponential smoothing is not suitable for data with seasonal or cyclical patterns, is sensitive to the choice of the smoothing parameter, does not incorporate any external factors or scenarios that may influence the future demand, and tends to underestimate or overestimate the forecast when there are sudden shifts or shocks in the data.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you use exponential smoothing to forecast future demand?

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1 What is exponential smoothing?

2 How does exponential smoothing work?

3 How can you choose the smoothing parameter?

4 How can you apply exponential smoothing to your data?

5 What are the advantages and limitations of exponential smoothing?

6 Here’s what else to consider

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How can you use exponential smoothing to forecast future demand?

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6

1 What is exponential smoothing?

2 How does exponential smoothing work?

3 How can you choose the smoothing parameter?

4 How can you apply exponential smoothing to your data?

5 What are the advantages and limitations of exponential smoothing?

6 Here’s what else to consider

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