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References

Consolidated bibliography across the knowledgebase. 66 unique sources cited as of 2026-05-15.

Each entry lists the canonical source, its URL, the date we last verified the URL, and the method pages that cite it. The per-method **Source:** / **Link:** / **Retrieved:** lines in methods/*.md are the source of truth — this page is a deduplicated index over them.

Adams, R. P. & MacKay, D. J. C., "Bayesian Online Changepoint Detection" (2007)

Ansari, Stella, Turkmen, Zhang, Mercado, Shen, Shchur, Rangapuram, Pineda Arango, Kapoor, Zschiegner, Maddix, Mahoney, Torkkola, Wilson, Bohlke-Schneider & Wang 2024, "Chronos: Learning the Language of Time Series"

Assimakopoulos & Nikolopoulos (2000), International Journal of Forecasting 16(4):521-530; winner of the M3 competition

Bandara, Hyndman & Bergmeir 2021, MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns

Bates, J. M. & Granger, C. W. J., "The Combination of Forecasts" (Operational Research Quarterly, 1969)

Brodersen, Gallusser, Koehler, Remy, Scott (2015), "Inferring causal impact using Bayesian structural time-series models," Annals of Applied Statistics 9(1):247-274

Challu, Olivares, Oreshkin, Garza, Mergenthaler & Dubrawski 2022, "N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting"

Classical recursive splitting (Scott & Knott 1974; popularised by Sen & Srivastava 1975). Implementation reference: ruptures docs.

Cleveland, Cleveland, McRae & Terpenning 1990; Hyndman & Athanasopoulos, Forecasting: Principles and Practice (fpp3) ch. 3.6

Common ML-forecasting practice; e.g., Lim et al. (2021) "Temporal Fusion Transformers"; multi-horizon Quantile Regression with horizon-wise penalties; see also sktime forecasting cookbook

Conceptual framework — see Bishop, "Pattern Recognition and Machine Learning" §3.3, and Koks & Challa, "An Introduction to Bayesian and Dempster-Shafer Data Fusion" (2003). PyMC implementation reference: PyMC Discourse "Bayesian data fusion" threads.

Croston, J.D. (1972), "Forecasting and Stock Control for Intermittent Demands," Operational Research Quarterly 23(3):289-303

Croston (1972), Operational Research Quarterly 23(3):289-303; Syntetos & Boylan (2005) SBA bias correction. Full entries on the intermittent methods page.

Cuturi & Blondel 2017, Soft-DTW: a Differentiable Loss Function for Time-Series (ICML)

Das, Kong, Sen & Zhou 2024, "A decoder-only foundation model for time-series forecasting"

De Livera, Hyndman & Snyder 2011, Forecasting time series with complex seasonal patterns using exponential smoothing

Doucet, A., de Freitas, N. & Gordon, N., "Sequential Monte Carlo Methods in Practice" (Springer, 2001)

Durbin, J. & Koopman, S. J., "Time Series Analysis by State Space Methods" (2nd ed., 2012)

Garza & Mergenthaler-Canseco 2023, "TimeGPT-1"

Gelman, Carlin, Stern, Dunson, Vehtari, Rubin, Bayesian Data Analysis (3rd ed.), ch. 5; canonical "eight schools" example

Gelman et al. Bayesian Data Analysis (3rd ed.); Salvatier, Wiecki & Fonnesbeck 2016 (PyMC); Phan, Pradhan & Jankowiak 2019 (NumPyro)

Gneiting & Raftery 2007, Strictly Proper Scoring Rules, Prediction, and Estimation; GluonTS Evaluator docs

Gneiting & Raftery 2007, "Strictly Proper Scoring Rules, Prediction, and Estimation"

Gneiting, Balabdaoui & Raftery 2007, "Probabilistic Forecasts, Calibration and Sharpness"

Hyndman & Athanasopoulos, Forecasting: Principles and Practice (3rd ed.), §8.2 "Methods with trend"

Hyndman & Athanasopoulos, Forecasting: Principles and Practice (3rd ed.), §5.2 "Some simple forecasting methods"

Hyndman & Athanasopoulos, "Forecasting: Principles and Practice" (3rd ed.) §13.4 (combinations); Granger & Ramanathan, "Improved methods of combining forecasts" (J. Forecasting, 1984).

Hyndman & Athanasopoulos, fpp3 ch. 7.4 (useful predictors) and ch. 10.5 (dynamic harmonic regression)

Hyndman & Athanasopoulos, fpp3 ch. 13.1

Hyndman & Koehler 2006, Another look at measures of forecast accuracy; fpp3 ch. 5.8

Hyndman & Athanasopoulos, fpp3 ch. 5.10

Hyndman & Athanasopoulos, Forecasting: Principles and Practice (3rd ed.), ch.8 (ETS) and ch.9 (ARIMA)

Hyndman & Athanasopoulos, Forecasting: Principles and Practice (3rd ed.), §5.5

Killick, R., Fearnhead, P. & Eckley, I. A., "Optimal Detection of Changepoints with a Linear Computational Cost" (JASA, 2012)

Lim, Arık, Loeff & Pfister 2021, "Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting"

Makridakis, Spiliotis & Assimakopoulos 2020, The M4 Competition: 100,000 time series and 61 forecasting methods; Hyndman & Koehler 2006

Matteson, D. S. & James, N. A., "A nonparametric approach for multiple change point analysis of multivariate data" (JASA, 2014)

Montero-Manso, P., Athanasopoulos, G., Hyndman, R. J. & Talagala, T. S., "FFORMA: Feature-based forecast model averaging" (Int. J. Forecasting, 2020)

Montero-Manso, Athanasopoulos, Hyndman, Talagala (2020), "FFORMA: Feature-based forecast model averaging," International Journal of Forecasting 36(1):86-92 (2nd place M4); Smyl (2020), "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting" (ES-RNN, M4 winner)

Nikolopoulos, Syntetos, Boylan, Petropoulos, Assimakopoulos (2011), "An Aggregate-Disaggregate Intermittent Demand Approach (ADIDA) to forecasting," Journal of the Operational Research Society 62(3):544-554

Nixtla statsforecast (extension of Croston 1972 / SBA 2005 with parameter optimization)

Oreshkin, Carpov, Chapados & Bengio 2019, "N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting"

Page, E. S., "Continuous Inspection Schemes" (Biometrika, 1954)

Petropoulos & Kourentzes (2015), "Forecast combinations for intermittent demand," Journal of the Operational Research Society 66(6):914-924

Rasul, Ashok, Williams, Khorasani, Adamopoulos, Bhagwatkar, Biloš, Ghonia, Hassen, Schneider, Garg, Drouin, Chapados, Nevmyvaka & Rish 2023/2024, "Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting"

Romano, Patterson & Candès 2019, "Conformalized Quantile Regression"

Saatçi, Y., Turner, R. & Rasmussen, C. E., "Gaussian Process Change Point Models" (ICML, 2010)

Sakoe & Chiba 1978, Dynamic programming algorithm optimization for spoken word recognition

Salinas, Flunkert, Gasthaus & Januschowski 2017/2020, "DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks"

Shafer & Vovk 2008, "A Tutorial on Conformal Prediction"; Stankevičiūtė, Alaa & van der Schaar 2021, "Conformal Time-Series Forecasting"

Standard post-processing after change-point detection; STL residual analysis. See Cleveland et al., "STL: A seasonal-trend decomposition procedure based on loess" (1990) and Hyndman & Athanasopoulos, "Forecasting: Principles and Practice" §3.4.

Standard statistics (Pearson 1895; Spearman 1904)

Stein's paradox / James-Stein estimator (1961); Efron (2010), Large-Scale Inference, ch. 1-3. Shares the same conceptual core as empirical-Bayes priors and hierarchical pooling on the short history page.

Syntetos & Boylan (2001, 2005), "The accuracy of intermittent demand estimates," International Journal of Forecasting 21(2):303-314

Syntetos, Boylan & Croston (2005), "On the categorization of demand patterns," Journal of the Operational Research Society 56(5):495-503

Taylor, S. J. & Letham, B., "Forecasting at Scale" (2018) — describes Prophet's changepoint mechanism.

Taylor & Letham 2018, Forecasting at Scale

Teunter, Syntetos, Babai (2011), "Intermittent demand: Linking forecasting to inventory obsolescence," European Journal of Operational Research 214(3):606-615

Triebe, Hewamalage, Pilyugina, Laptev, Bergmeir & Rajagopal 2021, "NeuralProphet: Explainable Forecasting at Scale"

US Census Bureau

Wang, Smith & Hyndman 2006, Characteristic-based clustering for time series data; Hyndman & Athanasopoulos, fpp3 §4.3

Wen, Torkkola, Narayanaswamy & Madeka 2017, "A Multi-Horizon Quantile Recurrent Forecaster" (MQRNN)

West, M. & Harrison, J., "Bayesian Forecasting and Dynamic Models" (2nd ed., Springer, 1997)

Wickramasuriya, S. L., Athanasopoulos, G. & Hyndman, R. J., "Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization" (JASA, 2019)

Woo, Liu, Kumar, Xiong, Savarese & Sahoo 2024, "Unified Training of Universal Time Series Forecasting Transformers"