Agarwal, Anish, Muhammad Jehangir Amjad, Devavrat Shah, and Dennis Shen. 2018.
“Time Series Analysis via Matrix Estimation.” February 25, 2018.
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.
Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. 2015.
“Inferring Causal Impact Using Bayesian Structural Time-Series Models.” The Annals of Applied Statistics 9 (1): 247–74.
https://doi.org/10.1214/14-AOAS788.
Broersen, Petrus MT. 2006.
Automatic Autocorrelation and Spectral Analysis.
Secaucus, NJ, USA:
Springer.
http://dsp-book.narod.ru/AASA.pdf.
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 10, 2019.
http://arxiv.org/abs/1903.03986.
Ding, J., V. Tarokh, and Y. Yang. 2018.
“Model Selection Techniques: An Overview.” IEEE Signal Processing Magazine 35 (6): 16–34.
https://doi.org/10.1109/MSP.2018.2867638.
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.
Lunde, Robert. 2019.
“Sample Splitting and Weak Assumption Inference For Time Series.” February 20, 2019.
http://arxiv.org/abs/1902.07425.
Lunde, Robert, and Cosma Rohilla Shalizi. 2017.
“Bootstrapping Generalization Error Bounds for Time Series.” November 8, 2017.
http://arxiv.org/abs/1711.02834.
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.
Nicholson, William B., Ines Wilms, Jacob Bien, and David S. Matteson. 2020.
“High Dimensional Forecasting via Interpretable Vector Autoregression.” Journal of Machine Learning Research 21 (166): 1–52.
http://jmlr.org/papers/v21/19-777.html.
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.
Ryabko, Daniil. 2009.
“On Finding Predictors for Arbitrary Families of Processes.” December 24, 2009.
http://arxiv.org/abs/0912.4883.
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.” July 24, 2009.
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 25, 2015.
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 29, 2017.
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.