Improving forecasting by subsampling seasonal time series

References

• Adya, M., F. Collopy, J. S. Armstrong, and M. Kennedy. 2001. “Automatic Identification of Time Series Features for Rule-Based Forecasting.” International Journal of Forecasting 17 (2): 143–157.
   Web of Science ®Google Scholar
• Assimakopoulos, V., and K. Nikolopoulos. 2000. “The Theta Model: A Decomposition Approach to Forecasting.” International Journal of Forecasting 16 (4): 521–530.
   Web of Science ®Google Scholar
• Athanasopoulos, G., R. J. Hyndman, N. Kourentzes, and F. Petropoulos. 2017. “Forecasting with Temporal Hierarchies.” European Journal of Operational Research 262 (1): 60–74.
   Web of Science ®Google Scholar
• Bates, J. M., and C. W. J. Granger. 1969. “The Combination of Forecasts.” Journal of the Operational Research Society 20 (4): 451–468.
   Google Scholar
• Bergmeir, C., R. J. Hyndman, and J. M. Benítez. 2016. “Bagging Exponential Smoothing Methods Using STL Decomposition and Box–Cox Transformation.” International Journal of Forecasting 32 (2): 303–312.
   Web of Science ®Google Scholar
• Billah, B., M. L. King, R. Snyder, and A. B. Koehler. 2006. “Exponential Smoothing Model Selection for Forecasting.” International Journal of Forecasting 22 (2): 239–247.
   Web of Science ®Google Scholar
• Bishop, C. M. 2006. Pattern Recognition and Machine Learning. Springer. https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/.
   Google Scholar
• Box, G. E., and N. R. Draper. 1987. Empirical Model-Building and Response Surfaces. Wiley Series in Probability and Mathematical Statistics. New York: Wiley.
   Google Scholar
• Chen, T., and C. Guestrin. 2016. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Francisco California USA. August 13 - August 17, 2016.
   Google Scholar
• Claeskens, G., J. R. Magnus, A. L. Vasnev, and W. Wang. 2016. “The Forecast Combination Puzzle: A Simple Theoretical Explanation.” International Journal of Forecasting 32 (3): 754–762.
   Web of Science ®Google Scholar
• Collopy, F., and J. S. Armstrong. 1992. “Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations.” Management Science 38 (10): 1394–1414.
   Web of Science ®Google Scholar
• De Livera, A. M., R. J. Hyndman, and R. D. Snyder. 2011. “Forecasting Time Series with Complex Seasonal Patterns Using Exponential Smoothing.” Journal of the American Statistical Association 106 (496): 1513–1527.
   Web of Science ®Google Scholar
• Doganis, P., E. Aggelogiannaki, and H. Sarimveis. 2008. “A Combined Model Predictive Control and Time Series Forecasting Framework for Production-Inventory Systems.” International Journal of Production Research 46 (24): 6841–6853.
   Web of Science ®Google Scholar
• Fildes, R., and F. Petropoulos. 2015. “Simple Versus Complex Selection Rules for Forecasting Many Time Series.” Journal of Business Research 68 (8): 1692–1701.
   Web of Science ®Google Scholar
• Fiorucci, J. A., T. R. Pellegrini, F. Louzada, F. Petropoulos, and A. B. Koehler. 2016. “Models for Optimising the Theta Method and Their Relationship to State Space Models.” International Journal of Forecasting 32 (4): 1151–1161.
   Web of Science ®Google Scholar
• Gardner, Jr., E. S. 2006. “Exponential Smoothing: The State of the Art – Part II.” International Journal of Forecasting 22 (4): 637–666.
   Web of Science ®Google Scholar
• Genre, V., G. Kenny, A. Meyler, and A. Timmermann. 2013. “Combining Expert Forecasts: Can Anything Beat the Simple Average?” International Journal of Forecasting 29 (1): 108–121.
   Web of Science ®Google Scholar
• Gneiting, T., and A. E. Raftery. 2007. “Strictly Proper Scoring Rules, Prediction, and Estimation.” Journal of the American Statistical Association 102 (477): 359–378.
   Web of Science ®Google Scholar
• Harvey, D., S. Leybourne, and P. Newbold. 1997. “Testing the Equality of Prediction Mean Squared Errors.” International Journal of Forecasting 13 (2): 281–291.
   Web of Science ®Google Scholar
• Hasni, M., M. Aguir, M. Babai, and Z. Jemai. 2019. “Spare Parts Demand Forecasting: A Review on Bootstrapping Methods.” International Journal of Production Research 57 (15-16): 4791–4804.
   Web of Science ®Google Scholar
• Hibon, M., and T. Evgeniou. 2005. “To Combine or Not to Combine: Selecting Among Forecasts and their Combinations.” International Journal of Forecasting 21 (1): 15–24.
   Web of Science ®Google Scholar
• Hyndman, R. J. 2011. “Cyclic and Seasonal Time Series.” https://robjhyndman.com/hyndsight/cyclicts/.
   Google Scholar
• Hyndman, R. 2018. “Mcomp: Data from the M-Competitions.” R Package Version 2.8. https://CRAN.R-project.org/package=Mcomp.
   Google Scholar
• Hyndman, R. J., and G Athanasopoulos. 2018. Forecasting:Principles and Practice. 2nd ed. Melbourne: OTexts.
   Google Scholar
• Hyndman, R., G. Athanasopoulos, C. Bergmeir, G. Caceres, L. Chhay, M. O'Hara-Wild, F. Petropoulos, et al. 2020. “Forecast: Forecasting Functions for Ttime Series and Linear Models.” R Package Version 8.13. http://pkg.robjhyndman.com/forecast.
   Google Scholar
• Hyndman, R. J., and Y. Khandakar. 2008. “Automatic Time Series Forecasting: The Forecast Package for R.” Journal of Statistical Software 26 (3): 1–22.
   PubMed Web of Science ®Google Scholar
• Hyndman, R. J., and A. B. Koehler. 2006. “Another Look at Measures of Forecast Accuracy.” International Journal of Forecasting 22 (4): 679–688.
   Web of Science ®Google Scholar
• Hyndman, R., A. B. Koehler, J. K. Ord, and R. D Snyder. 2008. Forecasting with Exponential Smoothing: The State Space Approach. Berlin Germany: Springer.
   Google Scholar
• Hyndman, R. J., A. B. Koehler, R. D. Snyder, and S. Grose. 2002. “A State Space Framework for Automatic Forecasting Using Exponential Smoothing Methods.” International Journal of Forecasting18 (3): 439–454.
   Web of Science ®Google Scholar
• Jose, V. R. R., and R. L. Winkler. 2008. “Simple Robust Averages of Forecasts: Some Empirical Results.” International Journal of Forecasting 24 (1): 163–169.
   Web of Science ®Google Scholar
• Kang, Y., W. Cao, F. Petropoulos, and F. Li. 2021. “Forecast with Forecasts: Diversity Matters.” European Journal of Operational Research. https://www.sciencedirect.com/science/article/pii/S0377221721008730.
   Web of Science ®Google Scholar
• Kang, Y., R. J. Hyndman, and K. Smith-Miles. 2017. “Visualising Forecasting Algorithm Performance Using Time Series Instance Spaces.” International Journal of Forecasting 33 (2): 345–358.
   Web of Science ®Google Scholar
• Kolassa, S. 2011. “Combining Exponential Smoothing Forecasts Using Akaike Weights.” International Journal of Forecasting 27 (2): 238–251.
   Web of Science ®Google Scholar
• Kourentzes, N., and G. Athanasopoulos. 2021. “Elucidate Structure in Intermittent Demand Series.” European Journal of Operational Research 288 (1): 141–152.
   Web of Science ®Google Scholar
• Kourentzes, N., F. Petropoulos, and J. R. Trapero. 2014. “Improving Forecasting by Estimating Time Series Structural Components across Multiple Frequencies.” International Journal of Forecasting 30 (2): 291–302.
   Web of Science ®Google Scholar
• Li, X., Y. Kang, and F. Li. 2020. “Forecasting with Time Series Imaging.” Expert System with Applications 160: 113680.
   Web of Science ®Google Scholar
• Lichtendahl, K. C., and R. L. Winkler. 2020. “Why Do Some Combinations Perform Better than Others?” International Journal of Forecasting 36 (1): 142–149.
   Web of Science ®Google Scholar
• Makridakis, S., A. Andersen, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, and R. Winkler. 1982. “The Accuracy of Extrapolation (Time Series) Methods: Results of a Forecasting Competition.” Journal of Forecasting 1 (2): 111–153.
   Web of Science ®Google Scholar
• Makridakis, S., and M. Hibon. 2000. “The M3-Competition: Results, Conclusions and Implications.” International Journal of Forecasting 16 (4): 451–476.
   Web of Science ®Google Scholar
• Makridakis, S., E. Spiliotis, and V. Assimakopoulos. 2020. “The M4 Competition: 100, 000 Time Series and 61 Forecasting Methods.” International Journal of Forecasting 36 (1): 54–74.
   Web of Science ®Google Scholar
• Mircetic, D., B. Rostami-Tabar, S. Nikolicic, and M. Maslaric. 2021. “Forecasting Hierarchical Time Series in Supply Chains: An Empirical Investigation.” International Journal of Production Research 18 (1): 1–20.
   Web of Science ®Google Scholar
• Montero-Manso, P., G. Athanasopoulos, R. J. Hyndman, and T. S. Talagala. 2020. “FFORMA: Feature-Based Forecast Model Averaging.” International Journal of Forecasting 36 (1): 86–92.
   Web of Science ®Google Scholar
• Montero-Manso, P., C. Netto, and T. S. Talagala. 2018. “M4comp2018: Data from the M4-Competition.” R Package Version: 0.1.0.
   Google Scholar
• Nikolopoulos, K., A. A. Syntetos, J. E. Boylan, F. Petropoulos, and V. Assimakopoulos. 2011. “An Aggregate-Disaggregate Intermittent Demand Approach (ADIDA) to Forecasting: An Empirical Proposition and Analysis.” The Journal of the Operational Research Society 62 (3): 544–554.
   Web of Science ®Google Scholar
• Petropoulos, F., D. Apiletti, V. Assimakopoulos, M. Z. Babai, D. K. Barrow, S. Ben Taieb, C. Bergmeir, et al. 2021. “Forecasting: Theory and Practice.” International Journal of Forecasting
   Web of Science ®Google Scholar
• Petropoulos, F., R. J. Hyndman, and C. Bergmeir. 2018. “Exploring the Sources of Uncertainty: Why Does Bagging for Time Series Forecasting Work?” European Journal of Operational Research 268 (2): 545–554.
   Web of Science ®Google Scholar
• Petropoulos, F., and N. Kourentzes. 2014. “Improving Forecasting via Multiple Temporal Aggregation.” Foresight: The International Journal of Applied Forecasting 34: 12–17.
   Google Scholar
• Petropoulos, F., and N. Kourentzes. 2015. “Forecast Combinations for Intermittent Demand.” The Journal of the Operational Research Society 66 (6): 914–924.
   Web of Science ®Google Scholar
• Petropoulos, F., S. Makridakis, V. Assimakopoulos, and K. Nikolopoulos. 2014. “‘Horses for Courses’ in Demand Forecasting.” European Journal of Operational Research 237 (1): 152–163.
   Web of Science ®Google Scholar
• Petropoulos, F., and I. Svetunkov. 2020. “A Simple Combination of Univariate Models.” International Journal of Forecasting 36 (1): 110–115.
   Web of Science ®Google Scholar
• Petropoulos, F., X. Wang, and S. M. Disney. 2019. “The Inventory Performance of Forecasting Methods: Evidence From the M3 Competition Data.” International Journal of Forecasting 35 (1): 251–265.
   Web of Science ®Google Scholar
• Reid, D. 1972. "A comparison of forecasting techniques on economic time series.” In Forecasting in action, edited by M. J. Bramson, I.G. Helps, and J.A.C.C. Watson-Grady. Birmingham, UK: Operational Research Society.
   Google Scholar
• Rendon-Sanchez, J. F., and L. M. de Menezes. 2019. “Structural Combination of Seasonal Exponential Smoothing Forecasts Applied to Load Forecasting.” European Journal of Operational Research 275 (3): 916–924.
   Web of Science ®Google Scholar
• Smith, J., and K. F. Wallis. 2009. “A Simple Explanation of the Forecast Combination Puzzle.” Oxford Bulletin of Economics and Statistics 71 (3): 331–355.
   Web of Science ®Google Scholar
• Spiliotis, E., F. Petropoulos, and V. Assimakopoulos. 2019. “Improving the Forecasting Performance of Temporal Hierarchies.” PloS One 14 (10): e0223422.
   PubMed Web of Science ®Google Scholar
• Svetunkov, I., and F. Petropoulos. 2018. “Old Dog, New Tricks: A Modelling View of Simple Moving Averages.” International Journal of Production Research 56 (18): 6034–6047.
   Web of Science ®Google Scholar
• Talagala, T. S., R. J. Hyndman, and G. Athanasopoulos. 2018. “Meta-Learning How to Forecast Time Series.” Technical Report. Monash University, Department of Econometrics and Business Statistics.
   Google Scholar
• Tashman, L. J. 2000. “Out-of-Sample Tests of Forecasting Accuracy: An Analysis and Review.” International Journal of Forecasting 16 (4): 437–450.
   Web of Science ®Google Scholar
• Taylor, J. W. 2003. “Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing.” Journal of the Operational Research Society 54 (8): 799–805.
   Web of Science ®Google Scholar
• Thomson, M. E., A. C. Pollock, D. Önkal, and M. S. Gönül. 2019. “Combining Forecasts: Performance and Coherence.” International Journal of Forecasting 35 (2): 474–484.
   Web of Science ®Google Scholar
• Wang, X., and F. Petropoulos. 2016. “To Select or to Combine? The Inventory Performance of Model and Expert Forecasts.” International Journal of Production Research 54 (17): 5271–5282.
   Web of Science ®Google Scholar
• Watson, M. W., and J. H. Stock. 2004. “Combination Forecasts of Output Growth in a Seven-Country Data Set.” Journal of Forecasting 23 (6): 405–430.
   Web of Science ®Google Scholar
• Winters, P. R. 1960. “Forecasting Sales by Exponentially Weighted Moving Averages.” Management Science 6 (3): 324–342.
   Web of Science ®Google Scholar
• Wolpert, D. H. 1996. “The Lack of a Priori Distinctions Between Learning Algorithms.” Neural Computation 8 (7): 1341–1390.
   Web of Science ®Google Scholar