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Ivan Svetunkov edited this page Jun 16, 2026 · 5 revisions

Academic References and Publications

This page maps each primary function in the smooth package to its underlying published research and reference materials.

Primary Reference

Svetunkov, I. (2023). Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) (1st ed.). Chapman and Hall/CRC.

Function-to-Reference Mapping

ADAM - Augmented Dynamic Adaptive Model

The unifying framework encompassing ETS, ARIMA, and regression models.

Primary Reference:

  • Svetunkov, I. (2023). Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM). Chapman and Hall/CRC.

Relevant Chapters:

  • Chapters 5-7: Pure Additive, Pure Multiplicative, and General ETS Models
  • Chapter 9: ADAM ARIMA
  • Chapter 10: Explanatory Variables
  • Chapter 11: Estimation
  • Chapter 12: Multiple Frequencies
  • Chapter 13: Intermittent State Space Model
  • Chapters 14-18: Diagnostics, Model Selection, Uncertainty, Scale Models, Forecasting

ES - Exponential Smoothing

References:

  • Svetunkov, I. (2023). Smooth forecasting with the smooth package in R. arXiv:2301.01790
  • Hyndman, R.J., Koehler, A.B., Ord, J.K., & Snyder, R.D. (2008). Forecasting With Exponential Smoothing: The State Space Approach. Springer. DOI: 10.1007/978-3-540-71918-2

CES - Complex Exponential Smoothing

References:

  • Svetunkov, I., Kourentzes, N., & Ord, J.K. (2022). Complex exponential smoothing. Naval Research Logistics, 69(5), 697-717. DOI: 10.1002/nav.22074

SSARIMA & MSARIMA - State Space ARIMA

References:

  • Svetunkov, I., & Boylan, J.E. (2019). State-space ARIMA for supply-chain forecasting. International Journal of Production Research, 58(3), 818-827. DOI: 10.1080/00207543.2019.1600764

SMA - Simple Moving Average

Reference:

  • Svetunkov, I., & Petropoulos, F. (2018). Old dog, new tricks: a modelling view of simple moving averages. International Journal of Production Research, 56(18), 6034-6047. DOI: 10.1080/00207543.2017.1380326

OES - Occurrence ETS

References:

  • Svetunkov, I., & Boylan, J.E. (2023). iETS: State space model for intermittent demand forecasting. International Journal of Production Economics, 265, 109013. DOI: 10.1016/j.ijpe.2023.109013

GUM - Generalised Univariate Model

References:

Additional Topics

Multi-Step Estimation

Reference:

  • Svetunkov, I., Kourentzes, N., & Killick, R. (2023). Multi-step Estimators and Shrinkage Effect in Time Series Models. Computational Statistics. DOI: 10.1007/s00180-023-01377-x

Pure Multiplicative ETS Models

Reference:

  • Svetunkov, I., & Boylan, J.E. (2024). Staying Positive: Challenges and Solutions in Using Pure Multiplicative ETS Models. IMA Journal of Management Mathematics, 35(1), 1-19. DOI: 10.1093/imaman/dpae002

Forecast Combination

References:

Foundational References

State Space Models and Exponential Smoothing

  • Hyndman, R.J., Koehler, A.B., Ord, J.K., & Snyder, R.D. (2008). Forecasting With Exponential Smoothing: The State Space Approach. Springer. DOI: 10.1007/978-3-540-71918-2

ARIMA and Box-Jenkins Methods

  • Box, G.E.P., & Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control (Revised ed.). Holden Day.

Decomposition Methods

Summary Table

Function Primary Application Key DOI/Reference
ADAM Unified ETS/ARIMA/Regression 10.1201/9781003452652
ES Exponential Smoothing 10.1007/978-3-540-71918-2
CES Complex Exponential Smoothing 10.1002/nav.22074
SSARIMA State Space ARIMA 10.1080/00207543.2019.1600764
SMA Simple Moving Average 10.1080/00207543.2017.1380326
OES Intermittent Demand 10.1016/j.ijpe.2023.109013
GUM Generalised Univariate Model OR Society 60 Conference
Multi-step Estimation Parameter Optimization 10.1007/s00180-023-01377-x
Prediction Intervals Uncertainty Quantification ADAM, Ch.16

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