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Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles: Comments
| Content Provider | Semantic Scholar |
|---|---|
| Author | Shen, Haipeng |
| Copyright Year | 2010 |
| Abstract | First, Professor Taylor ought to be congratulated onanother nice piece of work about exponential smooth-ing for intraday time series with multiple seasonal cy-cles. The methods discussed can be roughly groupedinto two categories, depending on how one views thetime series to be forecasted. Below I would like tobriefly discuss the two perspectives, as well as someconnection with functional data analysis and func-tional time series forecasting.Similar time series exist in multiple applications,including electricity demand and hospital emergencyroom patient arrivals, beside the call center volumesanalyzed in the paper. A distinguishing feature of thistype of data that needs to be incorporated by a suc-cessful forecaster is the multi-seasonality. For exam-ple, the call center arrival volume data contain bothan intraday cycle and an intraweek cycle. A commonway of analyzing such data is to view the data as a“long” univariate time series with double seasonality.This is the approach taken by the first four exponen-tially weighted methods of Taylor (2010), as well asthe methods of Taylor (2008).Alternatively, one could view the basic cycle (i.e.each day) as the basic data unit, and split the univari-ate time series into daily segments. The sequence of |
| Starting Page | 652 |
| Ending Page | 654 |
| Page Count | 3 |
| File Format | PDF HTM / HTML |
| DOI | 10.1016/j.ijforecast.2010.05.011 |
| Alternate Webpage(s) | http://www.unc.edu/~haipeng/publication/IJF-discussion.pdf |
| Alternate Webpage(s) | https://doi.org/10.1016/j.ijforecast.2010.05.011 |
| Volume Number | 26 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
