Time Series Data Forecasting

Time Series Data Forecasting

Vincent Cho (The Hong Kong Polytechnic University, Hong Kong)
Copyright: © 2005 |Pages: 5
DOI: 10.4018/978-1-59140-557-3.ch211
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Businesses are recognizing the value of data as a strategic asset. This is reflected by the high degree of interest in new technologies such as data mining. Corporations in banking, insurance, retail, and healthcare are harnessing aggregated operational data to help understand and run their businesses (Brockett et al., 1997; Delmater & Hamcock, 2001). Analysts use data-mining techniques to extract business information that enables better decision making (Cho et al., 1998; Cho & Wüthrich, 2002). In particular, time series forecasting is one of the major focuses in data mining. Time series forecasting is used in a variety of fields, such as agriculture, business, economics, engineering, geophysics, medical studies, meteorology, and social sciences. A time series is a sequence of data ordered in time, such as hourly temperature, daily stock prices, monthly sales, quarterly employment rates, yearly population changes, and so forth.

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