Time series prediction niceties.
🏗 handball to Rob Hyndman.
Here’s some miscellaneous generic stuff:
prophet (R/Python/Stan)
is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers.
Automatic Smoothing for Attention Prioritization in Time Series
ASAP automatically smooths time series plots to remove short-term noise while retaining large-scale deviations.
Agarwal, Anish, Muhammad Jehangir Amjad, Devavrat Shah, and Dennis Shen. 2018. “Time Series Analysis via Matrix Estimation,” February. http://arxiv.org/abs/1802.09064.
Alquier, Pierre, Xiaoyin Li, and Olivier Wintenberger. 2013. “Prediction of Time Series by Statistical Learning: General Losses and Fast Rates.” Dependence Modeling 1: 65–93. https://doi.org/10.2478/demo-2013-0004.
Alquier, Pierre, and Olivier Wintenberger. 2012. “Model Selection for Weakly Dependent Time Series Forecasting.” Bernoulli. http://arxiv.org/abs/0902.2924.
Ben Taieb, Souhaib, and Amir F. Atiya. 2016. “A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting.” IEEE Transactions on Neural Networks and Learning Systems 27 (1): 62–76. https://doi.org/10.1109/TNNLS.2015.2411629.
Bergmeir, Christoph, Rob J. Hyndman, and Bonsoo Koo. 2018. “A Note on the Validity of Cross-Validation for Evaluating Autoregressive Time Series Prediction.” Computational Statistics & Data Analysis 120 (April): 70–83. https://doi.org/10.1016/j.csda.2017.11.003.
Box, George E. P., Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung. 2016. Time Series Analysis: Forecasting and Control. Fifth edition. Wiley Series in Probability and Statistics. Hoboken, New Jersey: John Wiley & Sons, Inc.
Chevillon, Guillaume. 2007. “Direct Multi-Step Estimation and Forecasting.” Journal of Economic Surveys 21 (4): 746–85. https://doi.org/10.1111/j.1467-6419.2007.00518.x.
Commandeur, Jacques J. F., and Siem Jan Koopman. 2007. An Introduction to State Space Time Series Analysis. 1 edition. Oxford ; New York: Oxford University Press.
Commandeur, Jacques J. F., Siem Jan Koopman, and Marius Ooms. 2011. “Statistical Software for State Space Methods.” Journal of Statistical Software 41 (1). https://doi.org/10.18637/jss.v041.i01.
Cox, D. R., Gudmundur Gudmundsson, Georg Lindgren, Lennart Bondesson, Erik Harsaae, Petter Laake, Katarina Juselius, and Steffen L. Lauritzen. 1981. “Statistical Analysis of Time Series: Some Recent Developments [with Discussion and Reply].” Scandinavian Journal of Statistics 8 (2): 93–115. http://www.jstor.org/stable/4615819.
Dahl, Astrid, and Edwin V. Bonilla. 2019. “Sparse Grouped Gaussian Processes for Solar Power Forecasting,” March. http://arxiv.org/abs/1903.03986.
Gerstenberger, Matthew C., Stefan Wiemer, Lucile M. Jones, and Paul A. Reasenberg. 2005. “Real-Time Forecasts of Tomorrow’s Earthquakes in California.” Nature 435 (7040): 328–31. https://doi.org/10.1038/nature03622.
Granger, C. W. J., and Roselyne Joyeux. 1980. “An Introduction to Long-Memory Time Series Models and Fractional Differencing.” Journal of Time Series Analysis 1 (1): 15–29. https://doi.org/10.1111/j.1467-9892.1980.tb00297.x.
Hurvich, Clifford M. 2002. “Multistep Forecasting of Long Memory Series Using Fractional Exponential Models.” International Journal of Forecasting, Forecasting Long Memory Processes, 18 (2): 167–79. https://doi.org/10.1016/S0169-2070(01)00151-0.
Kurniasih, Nuning. n.d. “Knowledge Management of Agricultural Prophecy in the Manuscript of Sundanese Society in Tasikmalaya District of West Java Indonesia.” Accessed February 12, 2019. https://doi.org/10.31219/osf.io/uedxw.
Kuznetsov, Vitaly, and Mehryar Mohri. 2014. “Forecasting Non-Stationary Time Series: From Theory to Algorithms.” http://www.cims.nyu.edu/~munoz/multitask/Paper_22_fts.pdf.
———. 2015. “Learning Theory and Algorithms for Forecasting Non-Stationary Time Series.” In Advances in Neural Information Processing Systems, 541–49. Curran Associates, Inc. http://papers.nips.cc/paper/5836-learning-theory-and-algorithms-for-forecasting-non-stationary-time-series.
Moradkhani, Hamid, Soroosh Sorooshian, Hoshin V. Gupta, and Paul R. Houser. 2005. “Dual State–Parameter Estimation of Hydrological Models Using Ensemble Kalman Filter.” Advances in Water Resources 28 (2): 135–47. https://doi.org/10.1016/j.advwatres.2004.09.002.
Morvai, Gusztáv, Sidney Yakowitz, and László Györfi. 1996. “Nonparametric Inference for Ergodic, Stationary Time Series.” The Annals of Statistics 24 (1): 370–79. https://doi.org/10.1214/aos/1033066215.
Phillips, Robert F. 1987. “Composite Forecasting: An Integrated Approach and Optimality Reconsidered.” Journal of Business & Economic Statistics 5 (3): 389–95. https://doi.org/10.1080/07350015.1987.10509603.
Runge, Jakob, Reik V. Donner, and Jürgen Kurths. 2015. “Optimal Model-Free Prediction from Multivariate Time Series.” Physical Review E 91 (5). https://doi.org/10.1103/PhysRevE.91.052909.
Smith, Leonard A. 2000. “Disentangling Uncertainty and Error: On the Predictability of Nonlinear Systems.” In Nonlinear Dynamics and Statistics.
Sornette, Didier. 2009. “Dragon-Kings, Black Swans and the Prediction of Crises” 2 (1). http://arxiv.org/abs/0907.4290.
Sugihara, George. 1994. “Nonlinear Forecasting for the Classification of Natural Time Series.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 348 (1688): 477. https://doi.org/10.1098/rsta.1994.0106.
Taieb, Souhaib Ben, James W. Taylor, and Rob J. Hyndman. 2017. “Coherent Probabilistic Forecasts for Hierarchical Time Series.” In PMLR, 3348–57. http://proceedings.mlr.press/v70/taieb17a.html.
Taleb, Nassim Nicholas. 2018. “Election Predictions as Martingales: An Arbitrage Approach.” Quantitative Finance 18 (1): 1–5. https://doi.org/10.1080/14697688.2017.1395230.
Taylor, James W. 2008. “Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall.” Journal of Financial Econometrics 6 (3): 382–406. https://doi.org/10.1093/jjfinec/nbn007.
Taylor, Sean J., and Benjamin Letham. 2017. “Forecasting at Scale.” e3190v2. PeerJ Inc. https://doi.org/10.7287/peerj.preprints.3190v2.
Uematsu, Yoshimasa. 2015. “Penalized Likelihood Estimation in High-Dimensional Time Series Models and Its Application,” April. http://arxiv.org/abs/1504.06706.
Wang, Wei, David Rothschild, Sharad Goel, and Andrew Gelman. 2015. “Forecasting Elections with Non-Representative Polls.” International Journal of Forecasting 31 (3): 980–91. https://doi.org/10.1016/j.ijforecast.2014.06.001.
Wen, Ruofeng, Kari Torkkola, and Balakrishnan Narayanaswamy. 2017. “A Multi-Horizon Quantile Recurrent Forecaster,” November. http://arxiv.org/abs/1711.11053.
Werbos, Paul J. 1988. “Generalization of Backpropagation with Application to a Recurrent Gas Market Model.” Neural Networks 1 (4): 339–56. https://doi.org/10.1016/0893-6080(88)90007-X.
Werner, Maximilian J, Agnès Helmstetter, David Jackson, Yan Y Kagan, and Stefan Wiemer. 2010. “Adaptively Smoothed Seismicity Earthquake Forecasts for Italy.” Annals of Geophysics, no. 3 (November). https://doi.org/10.4401/ag-4839.