V.1:4 (90-91): Applying ARIMA Forecasts by ERIC WEISS, Ph.D.
Product Description
Applying ARIMA Forecasts
by ERIC WEISS, Ph.D.
"Applying ARIMA Forecasts" is an article continuing a series started in the October (82) and the January (83) issues of Technical Analysis. AutoRegressive-lntegrated Moving Average(ARIMA) is a forecasting methodology based upon the techniques described by Box and Jenkins in their book: Time Series Analysis, Forecasting and Control, published by Holden-Day, 1976, 2nd ed.
With understanding and time one may construct a model of a discrete statistical time series, such as the markets, that will forecast the future with known probable error.
To begin, let's use this example:
Variable: high
Sum of Squared Residuals: 44.76
Residual Variance: .75
Average Absolute Error: .65
Observations: 64
Degrees of Freedom: 60
"Applying ARIMA Forecasts" is an article continuing a series started in the October (82) and the January (83) issues of Technical Analysis. AutoRegressive-lntegrated Moving Average(ARIMA) is a forecasting methodology based upon the techniques described by Box and Jenkins in their book: Time Series Analysis, Forecasting and Control, published by Holden-Day, 1976, 2nd ed.
With understanding and time one may construct a model of a discrete statistical time series, such as the markets, that will forecast the future with known probable error.
To begin, let's use this example:
Variable: high
Sum of Squared Residuals: 44.76
Residual Variance: .75
Average Absolute Error: .65
Observations: 64
Degrees of Freedom: 60
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