Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing

By James W. Taylor, Ralph D. Snyder. 2009

89 citations recorded by the source corpus.

This paper concerns the forecasting of seasonal intraday time series. An extension of Holt-Winters exponential smoothing has been proposed that smoothes an intraday cycle and an intraweek cycle. A recently proposed exponential smoothing method involves smoothing a different intraday cycle for each distinct type of day of the week. Similar days are allocated identical intraday cycles. A limitation is that the method allows only whole days to be treated as identical. We introduce an exponential smoothing formulation that allows parts of different days of the week to be treated as identical. The result is a method that involves the smoothing and initialisation of fewer terms than the other two exponential smoothing methods. We evaluate forecasting up to a day ahead using two empirical studies. For electricity load data, the new method compares well with a range of alternatives. The second study involves a series of arrivals at a call centre that is open for a shorter duration at the weekends than on weekdays. By contrast with the previously proposed exponential smoothing methods, our new method can model in a straightforward way this situation, where the number of periods on each day of the week is not the same.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/befa13dbafddd252ec61ee4dd09531ce2f33b2b1

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In Electrical and Electronic Engineering

In Management Science and Operations Research

Citations in the corpus

Forecasting: theory and practice (2020, background): “…on the assumption of a time-inhomogeneous Poisson process adopting fixed (Jongbloed and Koole, 2001; Brown et al., 2005b; Shen and Huang, 2008a; Taylor, 2012) and mixed modelling (Avramidis et al., 2004; Aldor-Noiman et al., 2009; Ibrahim and L’Ecuyer, 2013) approaches to account for the…”
Pattern-based local linear regression models for short-term load forecasting (2016, methodology): “In [2] to reduce the dimension of the model new ES formulation called parsimonious seasonal ES was proposed.”
Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011, background): “Similarly, in modeling complex seasonality, the existing exponential smoothing models (e.g., Taylor 2003, 2010b; Gould et al. 2008; Taylor and Snyder 2009) suffer from various weaknesses such as overparameterization, and the inability to accommodate both non-integer period and dualcalendar effects.”