Resources

Resources

Academic references and learning resources cited across the greybox wiki, collected here so individual pages can link rather than repeat them.

Books and online texts

• 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.

Information criteria

• 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/

Half-moment measures

• 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.

Scoring rules and cross-validation

• 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.

Diagnostics

• Cook, R.D. and Weisberg, S. (1982). Residuals and Influence in Regression. Chapman and Hall. Background for diagnostics.

Software

• 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/config-i1/greybox