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Tutorial and Survey
Author(s) -
Ferratt Thomas W.,
Mabert Vincent A.
Publication year - 1972
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1972.tb01668.x
Subject(s) - autocorrelation , autocorrelation technique , computer science , matching (statistics) , series (stratigraphy) , partial autocorrelation function , identification (biology) , box–jenkins , sample (material) , statistics , econometrics , time series , mathematics , paleontology , botany , chemistry , chromatography , autoregressive integrated moving average , biology
This paper illustrates the use of the Box‐Jenkins methodology by analyzing the Ohio Electrical Power Consumption time series. The three basic analytical steps discussed are the following: 1) Model identification ‐ by matching sample autocorrelation functions against theoretical autocorrelation functions. 2) Non‐linear estimation of parameters ‐ by minimizing the sum of the squared residuals. 3) Diagnostic checking ‐ by analyzing the pattern of the autocorrelation function of the residuals. Regular and adaptive forecasts are then developed using the appropriate model that emerges from the time series analysis.