Edited by Larry Dwyer, Alison Gill and Neelu Seetaram
Shuang Cang and Neelu Seetaram INTRODUCTION ‘A time series typically consists of a set of observations made on a variable y, taken at equally spaced internals over time’ (Harvey, 1993, p. 1). The study of time series data is normally performed on two levels. Time series analysis comprises methods for extracting meaningful statistics and identifying key characteristics of the data. Time series modelling is performed in order to predict the future behaviour of y using historical data. The basis of time series modelling is that movement in y is explained solely by its past values or by its ‘position in relation [to] time’ (Harvey, 1993). Since time series models only require historical observations of a variable, it is less costly in terms of data collection and model estimation (Song and Li, 2008). In tourism research, both time series analysis and time series modelling have received great attention in the past three decades, perhaps due to the availability of relevant time series data. Common types of tourism time series are tourist arrivals, departures, expenditure levels, number of nights spent at a destination, price of hotel room, airfare, tourism contribution to the GDP and so on. These time series are mostly available on a monthly, quarterly and/or yearly basis. Frechtling (1996, 2001) highlighted five patterns in tourism time series: stationarity, seasonality, linear trend, non-linear trend and stepped series. The identification of these patterns can be an end in itself as seen by several studies which have attempted to describe the characteristics of...
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