tfarima-package: Transfer Function and ARIMA Models.
In tfarima: Transfer Function and ARIMA Models
tfarima-package R Documentation
Transfer Function and ARIMA Models.
Description
The tfarima package provides classes and methods to build customized transfer
function and ARIMA models with multiple operators and parameter restrictions.
The package also includes functions for model identification, model
estimation (exact or conditional maximum likelihood), model diagnostic
checking, automatic outlier detection, calendar effects, forecasting and
seasonal adjustment.
Author(s)
Jose Luis Gallego jose.gallego@unican.es
References
Bell, W.R. and Hillmer, S.C. (1983) Modeling Time Series with Calendar
Variation, Journal of the American Statistical Association, Vol. 78, No. 383,
pp. 526-534.
Box, G.E., Jenkins, G.M., Reinsel, G.C. and Ljung, G.M. (2015) Time Series
Analysis: Forecasting and Control. John Wiley & Sons, Hoboken.
Box, G.E.P., Pierce, D.A. and Newbold, D. A. (1987) Estimating Trend and
Growth Rates in Seasonal Time Series, Journal of the American Statistical
Association, Vol. 82, No. 397, pp. 276-282.
Box, G.E.P. and Tiao, G.C. (1975) “Intervention Analysis with Applications
to Economic and Environmental Problems”, Journal of the American Statistical
Association, Vol. 70, No. 349, pp. 70-79.
Chen, C. and Liu, L. (1993) Joint Estimation of Model Parameters and Outlier
Effects in Time Series, Journal of the American Statistical Association, Vol.
88, No. 421, pp. 284-297
Thompson, H. E. and Tiao, G. C. (1971) "Analysis of Telephone
Data: A Case Study of Forecasting Seasonal Time Series," Bell Journal of
Economics, The RAND Corporation, vol. 2(2), pages 515-541, Autumn.
tfarima documentation built on May 20, 2022, 5:06 p.m.
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| tfarima-package | R Documentation |
Transfer Function and ARIMA Models.
Description
The tfarima package provides classes and methods to build customized transfer function and ARIMA models with multiple operators and parameter restrictions. The package also includes functions for model identification, model estimation (exact or conditional maximum likelihood), model diagnostic checking, automatic outlier detection, calendar effects, forecasting and seasonal adjustment.
Author(s)
Jose Luis Gallego jose.gallego@unican.es
References
Bell, W.R. and Hillmer, S.C. (1983) Modeling Time Series with Calendar Variation, Journal of the American Statistical Association, Vol. 78, No. 383, pp. 526-534.
Box, G.E., Jenkins, G.M., Reinsel, G.C. and Ljung, G.M. (2015) Time Series Analysis: Forecasting and Control. John Wiley & Sons, Hoboken.
Box, G.E.P., Pierce, D.A. and Newbold, D. A. (1987) Estimating Trend and Growth Rates in Seasonal Time Series, Journal of the American Statistical Association, Vol. 82, No. 397, pp. 276-282.
Box, G.E.P. and Tiao, G.C. (1975) “Intervention Analysis with Applications to Economic and Environmental Problems”, Journal of the American Statistical Association, Vol. 70, No. 349, pp. 70-79.
Chen, C. and Liu, L. (1993) Joint Estimation of Model Parameters and Outlier Effects in Time Series, Journal of the American Statistical Association, Vol. 88, No. 421, pp. 284-297
Thompson, H. E. and Tiao, G. C. (1971) "Analysis of Telephone Data: A Case Study of Forecasting Seasonal Time Series," Bell Journal of Economics, The RAND Corporation, vol. 2(2), pages 515-541, Autumn.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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