Decomposition by causal forces: a procedure for forecasting complex time series
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- J. S. Armstrong, 2005. "Decomposition by Causal Forces: A Procedure for Forecasting Complex Time Series," General Economics and Teaching 0502015, University Library of Munich, Germany.
References listed on IDEAS
- JS Armstrong & Fred Collopy, 2004. "Causal Forces: Structuring Knowledge for Time-series Extrapolation," General Economics and Teaching 0412003, University Library of Munich, Germany.
- Fred Collopy & J. Scott Armstrong, 1992.
"Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations,"
Management Science, INFORMS, vol. 38(10), pages 1394-1414, October.
- Fred Collopy & JS Armstrong, 2004. "Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations," General Economics and Teaching 0412004, University Library of Munich, Germany.
- Ernst R. Berndt & Neal J. Rappaport, 2001. "Price and Quality of Desktop and Mobile Personal Computers: A Quarter-Century Historical Overview," American Economic Review, American Economic Association, vol. 91(2), pages 268-273, May.
- Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
- Armstrong, J Scott & Collopy, Fred, 2001. "Identification of Asymmetric Prediction Intervals through Causal Forces," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(4), pages 273-283, July.
- JS Armstrong & Fred Collopy, 2004. "Integration of Statistical Methods and Judgment for Time Series," General Economics and Teaching 0412024, University Library of Munich, Germany.
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Cited by:
- Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015.
"Golden rule of forecasting: Be conservative,"
Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
- Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2014. "Golden Rule of Forecasting: Be conservative," MPRA Paper 53579, University Library of Munich, Germany.
- Kirshners Arnis & Borisov Arkady, 2012. "A Comparative Analysis of Short Time Series Processing Methods," Information Technology and Management Science, Sciendo, vol. 15(1), pages 65-69, December.
- Anqiang Huang & Kin Keung Lai & Han Qiao & Shouyang Wang & Zhenji Zhang, 2018. "Does Interval Knowledge Sharpen Forecasting Models? Evidence from China’s Typical Ports," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 467-483, March.
- Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
- Germán Rubio Guerrero, 2017. "Perspectiva multivariante de los pronósticos en las pymes industriales de Ibagué (Colombia)," Revista Facultad de Ciencias Económicas, Universidad Militar Nueva Granada, vol. 25(2), pages 25-40, September.
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