Analysis of High-Frequency Seasonal Time Series

Ruey S. Tsay ()
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Ruey S. Tsay: University of Chicago, Booth School of Business

A chapter in Time Series and Wavelet Analysis, 2024, pp 3-26 from Springer

Abstract: Abstract Data of high-frequency time series are widely available nowadays. These data often exhibit certain features that are similar to, yet distinct from those of the low-frequency time series. Conventional time series methods thus become inadequate in analyzing such high-frequency data. In this chapter, we propose a structural approach to overcome the difficulties in analyzing high-frequency time series. In particular, a mixture of deterministic and stochastic components is used, in conjunction with conditional heteroscedasticity, to model the seasonality of the high-frequency series. The usefulness and applicability of the proposed approach is demonstrated by modeling hourly measurements of particulate matter with diameters 2.5 micrometers and smaller (PM 2.5 $${ }_{2.5}$$ ) series over a ten year period with sample size 87600. Extensions to modeling high-frequency seasonal spatio-temporal series are discussed.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-66398-7_1

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DOI: 10.1007/978-3-031-66398-7_1

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