FBE Endüstri Mühendisliği Lisansüstü Programı  Yüksek Lisans
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Konu "adaptive forecasting methodology" ile FBE Endüstri Mühendisliği Lisansüstü Programı  Yüksek Lisans'a göz atma
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ÖgeAn adaptive forecasting methodology by utilizing change point detection technique on time series(Institute of Science And Technology, 20200316) Naser Naemi Avval, Ali ; Beyca, Ömer Faruk ; 507161134 ; Industrial Engineering ; Endüstri MühendisliğiThe objective of the exponential smoothing forecasting is to use past observations to form future forecast, to do this algorithm use past data by multiplying specific weight of for each observation in order to magnify the importance the most recent observation compare to older ones, in most of the samples of past observations there are some abrupt change lying beyond which called time series data that called change points, they have a direct impact on forecast values and cause portion of errors called residual inside the estimated values, while it is data analysts monitor these changes by using several methods to clarify the reasons of the these outliers and preserve the operation or data points from further change points, removing these outliers from training sets of the forecasting algorithms can improve the efficiency of estimated values too. In this research we used Holtwinters and ARIMA method to forecast the next 30 day electricity consumption according to our data, such that we changed the process of the HoltWinters(HW) exponential smoothing forecast such that instead of fitting whole data points using HW we conducted a graph based CPD method, this method uses two sample test called minimum spinning tree(MST) to form a graphical view of data points to find two sample of data according to connection between data points. And as another change point detection approach we used dynamic time wrapping method to cluster the data so that we identified 9 outlier points and eliminated them from data. using the outcome of graphbased method which searches for single change point called τ it separates the whole data into two samples, one before the change point and the other after the change point, then HW conducts separately on two samples, while for one of the samples these are real data points instead of the second one we added fitted values of the second sample, comparing new outcomes with normal HW outcomes with real data points using mean absolute percentage error (MAPE), and also we used the outcome of the dynamic time wrapping and its forecast error to compare them with the graphbased method, these results suggest that new method lowers the difference between real values and forecasted values thus this method can cause more accurate results comparison with traditional ARIMA and exponential smoothing method.