INTRODUCTION
A time series is an ordered sequence of observations. This ordering is usually through time, although other dimensions, such as spatial ordering, are sometimes encountered. A time series can be continuous, as when an electrical signal such as voltage is recorded. Typically, however, most industrial time series are observed and recorded at specific time intervals and are said to be discrete time series. If only one variable is observed, the time series is said to be univariate. However, some time series involve simultaneous observations on several variables. These are called multivariate time series.
There are three general objectives for studying time series: 1) understanding and modeling of the underlying mechanism that generates the time series, 2) prediction of future values, and 3) control of some system for which the time series is a performance measure. Examples of the third application occur frequently in industry. Almost all time series exhibit some structural...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abraham, B. and Ledolter, J. (1983). Statistical Methods for Forecasting. John Wiley, New York.
Akaike, H. (1976). “Canonical Correlations Analysis of Time Series and the Use of an Information Criterion,” Advances and Case Studies in System Identification, R. Mehra and D.G. Lainiotis, eds., Academic Press, New York.
Box, G.E.P. and Jenkins, G.M. (1976). Time Series Analysis, Forecasting and Control, revised edition, Holden-Day, San Francisco.
Box, G.E.P. and Pierce, D.A. (1970). “Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models,” Jl. American Statistical Association, 64.
Box, G.E.P. and Tiao, G.C. (1975). “Intervention Analysis with Applications to Economic and Environmental Problems,” Jl. American Statistical Association, 70, 70–79.
Brown, R.G. (1962). Smoothing, Forecasting and Prediction of Discrete Time Series, Prentice-Hall, Englewood Cliffs, New Jersey.
Chang, I., Tiao, G.C., and Chen, C. (1988). “Estimations of Time Series Parameters in the Presence of Outliers,” Technometrics, 30, 193–204.
Cleveland, W.S. (1972). “The Inverse Autocorrelations of a Time Series and Their Applications,” Technometrics, 14, 277–293.
Cogger, K.O. (1974). “The Optimality of General-Order Exponential Smoothing,” Operations Research, 22, 858–867.
Fox, A.J. (1972). “Outliers in Time Series,” Jl. Royal Statistical Society, Ser. B, 43, 350–363.
Fuller, W.A. (1996). Introduction to Statistical Time Series, John Wiley, New York.
Goodman, J.L. (1974). “A New Look at Higher-Order Exponential Smoothing for Forecasting,” Operations Research, 22, 880–888.
Granger, G.W.C. and Newbold, P. (1977). Forecasting Economic Time Series, Academic Press, New York.
Hanan, E.J. (1970). Multiple Time Series, John Wiley, New York.
Harvey, A.C. (1981). The Econometric Analysis of Time Series, John Wiley, New York.
Holt, C.C. (1957). “Forecasting Trends and Seasonal by Exponentially Weighted Moving Averages,” ONR Memorandum No. 52, Carnegie Institute of Technology.
Jenkins, G.M. (1979). Practical Experiences with Modeling and Forecasting Time Series, GJM Publications, Lancaster, England.
Kalman, R.E. (1960). “A New Approach to Linear Filtering and Prediction Problems,” ASME Jl. Basic Engineering for Industry, Ser. D, 82, 35–45.
Ljung, G.M. and Box, G.E.P. (1978). “On a Measure of Lack of Fit in Time Series Models,” Biometrika, 65, 297–303.
Marquardt, D.W. (1963). “An Algorithm for least Squares Estimation of Nonlinear Parameters,” Jl. Society of Industrial and Applied Mathematics, 2, 431–441.
McKenzie, E. (1978). The Monitoring of Exponentially Weighted Forecasts,” Jl. Operational Research Society, 29.
Montgomery, D.C., Johnson, L.A., and Gardiner, J.S. (1990). Forecasting and Time Series Analysis, 2nd ed., McGraw-Hill, New York.
Montgomery, D.C. and Weatherby, G. (1980). “Modeling and Forecasting Time Series Using Transfer Function and Intervention Methods” AIIE Transactions, 12, 289–307.
Pandit, S.M. and Wu, S.M. (1974). “Exponential Smoothing as a Special Case of a Linear Stochastic System,” Operations Research, 22, 868–879.
Tsay, R.S. (1986). “Nonlinearity Tests for Time Series,” Biometrika, 73, 461–466.
Whittle, P. (1963). Prediction and Regulation by Linear Least-Square Methods. Van Nostrand, Princeton, New Jersey.
Wichern D.W. and Jones, R.H. (1977). “Assessing the Input of Market Disturbances Using Intervention Analysis,” Management Science, 21, 329–337.
Winters, P.R. (1960). “Forecasting Sales by Exponentially Weighted Moving Averages,” Operations Re-search, 22, 858–867.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Kluwer Academic Publishers
About this entry
Cite this entry
Mastrangelo, C.M., Simpson, J.R., Montgomery, D.C. (2001). Time series analysis. In: Gass, S.I., Harris, C.M. (eds) Encyclopedia of Operations Research and Management Science. Springer, New York, NY. https://doi.org/10.1007/1-4020-0611-X_1045
Download citation
DOI: https://doi.org/10.1007/1-4020-0611-X_1045
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-7923-7827-3
Online ISBN: 978-1-4020-0611-1
eBook Packages: Springer Book Archive