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Supply Chain Excellence
Published in James William Martin, Operational Excellence, 2021
The decomposition method breaks this time series' pattern into components. Table 13.8 shows the logic for doing a decomposition. The first step is to fit a moving average model to the time series with the same interval and periodicity (i.e., days, weeks, months, quarters, or years). In the current example, the interval is monthly sales, which has an annual seasonal pattern that repeats every twelve months. Creating a moving average model eliminates the seasonality of the original time series. When the moving average time series is subtracted from the original time series, the seasonal indices can be calculated. If the data set is quarterly, then there will be four seasonal indices. The decomposition method continues until all components have been isolated. The irregular component is the variation of sales for which the model cannot account (i.e., the model's error).
Sanitary sewer demand forecasting using artificial neural networks
Published in Mark Knight, Neil Thomson, Underground Infrastructure Research, 2020
S. Chung, D. Abraham, G. Hwang
However, the limited structure in time-sen es models results in poor performance for long-term forecasting (Pindyck and Rubinfeld 1991). In the moving average model, through a process of averaging, the random fluctuations in data are smoothed so that the trend is more easily discernible. This allows for the most recent data to be always included in the calculation process leading to greater accuracy. However, the drawback of moving average model is that all data get equal weight. This can lead to internal inconsistencies, as it would be expected that the latter data should be assigned higher weights to account for greater significance.
Forecasting the number of road accidents in Poland by day of the week and the impact of pandemics and pandemic-induced changes
Published in Cogent Engineering, 2023
Piotr Gorzelanczyk, Tomas Kalina, Martin Jurkovič, Malaya Mohanty
Biswas et al. (2019) used Random Forest regression to predict the number of road accidents. In another study, the data contain groups of correlated features with similar significance to the original data, where smaller groups are favoured over larger groups (Random forest, 2022), and there is instability in the method and spike prediction (Fijorek et al., 2010). Chudy-Laskowska and Pisula (2014) used the following in the prediction problem under discussion: an autoregressive model with quadratic trend, a model with univariate periodic trend and an exponential equalization model. A moving average model can be used for the prediction of the discussed issue; however, the disadvantages of the technique are low prediction accuracy, loss of data in the sequence, lack of consideration of trends and seasonal effects (Kaszpruk, 2010). Prochozka and Camej used the GARMA method. In this method, some restrictions are imposed in the parameter space to guarantee the stationarity of the process (Prochazka & Camaj, 2017). Very often, the ARMA model for a stationary process or ARIMA or SARIMA for a non-stationary process is used for forecasting (Dutta et al., 2020; Karlaftis & Vlahogianni, 2009; Procházka et al., 2017; Sunny et al., 2018). This results in very high flexibility of the discussed models, but it is also their disadvantage, as good model identification requires more experience from the researcher than, for example, regression analysis (Łobejko, 2015). Another disadvantage is the linear nature of the ARIMA model (Dudek, 2013).