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Optimizing smoothing parameters for the triple exponential forecasting model
Narasingaraj, Harish Balaji
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https://hdl.handle.net/2142/90834
Description
- Title
- Optimizing smoothing parameters for the triple exponential forecasting model
- Author(s)
- Narasingaraj, Harish Balaji
- Issue Date
- 2016-04-26
- Director of Research (if dissertation) or Advisor (if thesis)
- Nagi, Rakesh
- Department of Study
- Industrial&Enterprise Sys Eng
- Discipline
- Industrial Engineering
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- M.S.
- Degree Level
- Thesis
- Keyword(s)
- Holt Winters
- Triple Exponential Smoothing parameters
- M3 Competition
- Abstract
- Exponential smoothing has always been a popular topic of research in forecasting. The triple exponential smoothing in particular involves modeling a function that is a combination of level, trend and seasonal factors. While simulating the model, each of the factors is associated with a parameter whose value has a significant impact on the accuracy of the forecast, yet optimizing these parameters for a time series has received relatively little attention in literature. In this thesis we will explore the results of multi-step forecasting by using parameters optimized through an algorithm centered around h-step ahead errors. An empirical study conducted on forecasting the monthly time series from the M3-Competition across a range of horizons gave us promising results. We show that this method proves to be better than the standard Holt-Winters procedure for the entire forecasting horizon in five out the six categories of data considered . We also show that this method significantly improves the accuracy over the short term forecasting horizon when compared to the automated Holt-Winters procedure used by experts in the M3 competition. Encouraged by these results, we recommend replicating this methodology to other models of the triple exponential smoothing in the future.
- Graduation Semester
- 2016-05
- Type of Resource
- text
- Permalink
- http://hdl.handle.net/2142/90834
- Copyright and License Information
- Copyright 2016 Harish B Narasingaraj
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