Exponential smoothing, forecasting method

Step 3 Select any time series forecasting method. For illustration, we will use the exponential smoothing forecasting method with a = 0.2.For the initial forecast for Quarter 1 of year 2008, we will use 600. 

Univariate Forecasts of a given variable demand are based on a model fitted only to present and past observations of a given time series. There are several different univariate models, like Extrapolation of Trend Curves, Simple Exponential Smoothing, Holt Method, Holt-Winters Method, Box-Jenkins Procedure, and Stepwise Auto-regression, which can be regarded as a subset of the Box-Jenkins Procedure. 

ES Agent, finally, determines forecasts according to the simple exponential smoothing method, which estimates the demand in any period as the weighted average of the last period demand and the forecast of demand in that period. It can be expressed... 

To develop the tool, we have considered only simple forecasting methods, such as moving averages and exponential smoothing, so that each level of the chain uses the best one that suits the demand it should deal with. With them, it is possible to achieve great results in reducing Bullwhip Effect. Even so, we have also shown that the inclusion of more advanced forecasting methods (ARIMA models) allows an even better system performance. 

Zhu Qingming Hao Zhang. 2012. Study on the application of cubic exponential smoothing method in coal mine accidents forecasting. Joural of Safety Science and Technology (4) 105-107. 

Statistical forecast methods (e.g.. Exponential Smoothing, Box-Jenkins, Holt and Holt-Winters) are used to plan business volume for short term period (1 week to 4 months). Combined forecast methods are also used to improve forecast accuracy. 

Lawrence [68] uses a forecasting based approach for estimating flow times. In particular, he focuses on approximating the flow time estimation error distribution (which is assumed to be stationary, and denoted by G) by using the method of moments [111]. He uses six methods for forecasting flow times. NUL sets flow times to zero, such that G becomes an estimator for the flow time. ESF uses exponential smoothing, such that the flow time estimate after the completion of k jobs is fk = otFk + (1 — oc)fk-i- The other four rules... 

Using the sample data given in Table 2.1, determine the forecast for month 7 using the Exponential Smoothing method. 

Step 4 Apply the exponential smoothing method on the deseasonalized demand to get deseasonalized forecast as shown in Table 2.3. For example, deseasonalized forecast for Quarter 1 (2011) is given by... 

Regular exponential smoothing model estimates the constant level (L) to forecast future demands. Holt s model improves it by estimating both the level (L) and trend factor (T). It adjusts both the level and trend factor using exponential smoothing. Holt s method is also known as double exponential smoothing or trend adjusted exponential smoothing method. 

In Example 2.3, we then used the exponential smoothing method with a = 0.2 to determine the deseasonalized forecasts. Instead, we will apply Holt s method to incorporate trend also. 

A drawback of the previous approach is that the seasonality indices remain static and are not updated during the forecast horizon. Winters (1960) has extended Holt s method by updating seasonality indices also using exponential smoothing. 

Consider the actual demand, its forecast, and the errors for five periods given in Table 2.9. The forecasts are obtained using exponential smoothing method with a = 0.1. Compute MAD, MSE, STD, Bias, and MAPE for measuring the accuracy of the forecasting method. 

Interested readers can refer to the original paper by Croston (1972). Willemain et al. (1994) did a comparative evaluation of Croston s method in forecasting intermittent demand in manufacturing using industrial data. They concluded that Croston s method was superior to the exponential smoothing method and was robust even under situations when Croston s model assumptions were violated. 

Exponential smoothing is similar to the moving average methods but it eliminates some of the calculations. The model uses a smoothing factor (less than 1) for forecasting the next period activity. The mathematical formula is... 

If we have to fall back on forecast methods in sales planning, we recommend simple first-order exponential smoothing with the variable smoothing factor ALPHA. 

Forecast quarterly demand for year 5 using simple exponential smoothing with a = 0.1 as well as Holt s model with a = 0.1 and / = 0.1. Which of the two methods do you prefer Why ... 

Consider monthly demand for the ABC Coiporation as shown in Table 7-3. Forecast the monthly demand for Year 6 using moving average, simple exponential smoothing, Holt s model, and Winter s model. In each case, evaluate the bias, TS, MAD, MAPE, and MSE. Which forecasting method do you prefer Why ... 

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