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Ivan Svetunkov edited this page Jul 21, 2026
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Academic references and learning resources cited across the greybox wiki, collected here so individual pages can link rather than repeat them.
- Svetunkov, I. (2023). Statistics for Business Analytics. Online: https://openforecast.org/sba/. The primary companion text for greybox.
- Svetunkov, I. (2022). Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM). Chapman and Hall/CRC. Online: https://openforecast.org/adam/.
- Burnham, K.P. and Anderson, D.R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer-Verlag, New York. doi:10.1007/b97636. Basis for AIC/AICc-based selection (stepwise, CALM).
- Levenbach, H. (2021). Four P's in a Pod: e-Commerce Forecasting and Planning for Supply Chain Practitioners. Independently published. ISBN 979-8461733575. Source of the Seasonality/Trend/Irregular (STI) classification implemented in EDA.
- McQuarrie, A.D. (1999). A small-sample correction for the Schwarz SIC model
selection criterion. Statistics & Probability Letters, 44(1), pp.79-86.
doi:10.1016/S0167-7152(98)00294-6.
Basis for
BICc.
Forecast accuracy measures (measures)
- Hyndman, R.J. and Koehler, A.B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22, pp.679-688.
- Davydenko, A. and Fildes, R. (2013). Measuring Forecasting Accuracy: The Case Of Judgmental Adjustments To SKU-Level Demand Forecasts. International Journal of Forecasting, 29(3), pp.510-522. doi:10.1016/j.ijforecast.2012.09.002.
- Petropoulos, F. and Kourentzes, N. (2015). Forecast combinations for intermittent demand. Journal of the Operational Research Society, 66, pp.914-924.
- Wallstrom, P. and Segerstedt, A. (2010). Evaluation of forecasting error measurements and techniques for intermittent demand. International Journal of Production Economics, 128, pp.625-636.
- Kourentzes, N. (2014). The Bias Coefficient: a new metric for forecast bias. https://kourentzes.com/forecasting/2014/12/17/the-bias-coefficient-a-new-metric-for-forecast-bias/
- Svetunkov, I. (2017). Naughty APEs and the quest for the holy grail. https://openforecast.org/2017/07/29/naughty-apes-and-the-quest-for-the-holy-grail/
- Svetunkov, I., Kourentzes, N. and Svetunkov, S. (2023). Half Central Moment for Data Analysis. Working Paper of Department of Management Science, Lancaster University, 2023:3, pp.1-21.
- Gneiting, T. and Raftery, A.E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477), pp.359-378.
- Tashman, L.J. (2000). Out-of-sample tests of forecasting accuracy: an analysis and review. International Journal of Forecasting, 16(4), pp.437-450. Background for rolling_origin.
Smoothers (Smoothers)
- Cleveland, W.S. (1979). Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association, 74(368), pp.829-836. doi:10.1080/01621459.1979.10481038.
- Friedman, J.H. (1984). A Variable Span Smoother. Technical Report 5 (SLAC-PUB-3477; STAN-LCS-005), Laboratory for Computational Statistics, Stanford University. OSTI 1447470.
Demand analysis (AID)
- Svetunkov, I. and Sroginis, A. (2025). Why do zeroes happen? A model-based approach for demand classification. arXiv:2504.05894. doi:10.48550/arXiv.2504.05894.
- Cook, R.D. and Weisberg, S. (1982). Residuals and Influence in Regression. Chapman and Hall. Background for diagnostics.
- Svetunkov, I. greybox: Toolbox for Model Building and Forecasting. R package. https://cran.r-project.org/package=greybox
- greybox (Python). https://pypi.org/project/greybox/
- Source repository: https://github.com/openforecast-org/greybox