A Triginometric Seasonal Component Model and its Application to Time Series with Two Types of Seasonality

TL;DR

This paper introduces a finite trigonometric series model for seasonal time series, effectively capturing multiple seasonal patterns and simplifying modeling of regular seasonal data, demonstrated through various real-world datasets.

Contribution

It proposes a novel trigonometric seasonal component model suitable for multiple and simple seasonal patterns, enhancing time series analysis methods.

Findings

Effective modeling of two types of seasonality in real data

Simplifies representation of seasonal patterns with few components

Demonstrates applicability to diverse datasets like electricity and economic data

Abstract

A finite trigonometric series model for seasonal time series is considered in this paper. This component model is shown to be useful, in particular, for the modeling of time series with two types of seasonality, a long-period and a short period. This component model is also shown to be effective in the case of ordinary seasonal time series with only one seasonal component, if the seasonal pattern is simple and can be well represented by a small number of trigonometric components. As examples, electricity demand data, bi-hourly temperature data, CO2 data, and two economic time series are considered. The last section summarizes the findings from the emperical studies.