Prediction of hierarchical time series using structured regularization and its application to artificial neural networks
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[1] R. Lippmann,et al. An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.
[2] George Athanasopoulos,et al. Forecasting: principles and practice , 2013 .
[3] Jairo Cugliari,et al. Game-theoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts , 2015 .
[4] Ryuhei Miyashiro,et al. Best subset selection via cross-validation criterion , 2020 .
[5] P. Zhao,et al. The composite absolute penalties family for grouped and hierarchical variable selection , 2009, 0909.0411.
[6] Ken Kobayashi,et al. BEST SUBSET SELECTION FOR ELIMINATING MULTICOLLINEARITY , 2017 .
[7] James Bailey,et al. Topology-regularized universal vector autoregression for traffic forecasting in large urban areas , 2017, Expert Syst. Appl..
[8] Massimiliano Pontil,et al. Regularized multi--task learning , 2004, KDD.
[9] K. U.,et al. Variational Bayesian inference for forecasting hierarchical time series , 2014 .
[10] Takanobu Nakahara,et al. Investigating consumers’ store-choice behavior via hierarchical variable selection , 2017, Adv. Data Anal. Classif..
[11] Trevor Hastie,et al. Statistical Learning with Sparsity: The Lasso and Generalizations , 2015 .
[12] Rob J. Hyndman,et al. Coherent Probabilistic Forecasts for Hierarchical Time Series , 2017, ICML.
[13] Mehdi Khashei,et al. An artificial neural network (p, d, q) model for timeseries forecasting , 2010, Expert Syst. Appl..
[14] Rob J. Hyndman,et al. Fast computation of reconciled forecasts for hierarchical and grouped time series , 2016, Comput. Stat. Data Anal..
[15] George Athanasopoulos,et al. Hierarchical forecasts for Australian domestic tourism , 2009 .
[16] Kota KUDO,et al. Stochastic Discrete First-Order Algorithm for Feature Subset Selection , 2020, IEICE Trans. Inf. Syst..
[17] Richard A. Davis,et al. Time Series: Theory and Methods , 2013 .
[18] Enno Siemsen,et al. The Sum and Its Parts: Judgmental Hierarchical Forecasting , 2016 .
[19] R. Tibshirani,et al. A LASSO FOR HIERARCHICAL INTERACTIONS. , 2012, Annals of statistics.
[20] Gene Fliedner,et al. An investigation of aggregate variable time series forecast strategies with specific subaggregate time series statistical correlation , 1999, Comput. Oper. Res..
[21] Jean-Philippe Vert,et al. Clustered Multi-Task Learning: A Convex Formulation , 2008, NIPS.
[22] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[23] Helmut Lütkepohl,et al. Forecasting Aggregated Time Series Variables: A Survey , 2011 .
[24] Francis R. Bach,et al. Structured Variable Selection with Sparsity-Inducing Norms , 2009, J. Mach. Learn. Res..
[25] Jiafan Yu,et al. Regularization in Hierarchical Time Series Forecasting with Application to Electricity Smart Meter Data , 2017, AAAI.
[26] Sebastian Ruder,et al. An Overview of Multi-Task Learning in Deep Neural Networks , 2017, ArXiv.
[27] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[28] George Athanasopoulos,et al. Forecast reconciliation: A geometric view with new insights on bias correction , 2021, International Journal of Forecasting.
[29] Rob J. Hyndman,et al. Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization , 2018, Journal of the American Statistical Association.
[30] S. Karsoliya,et al. Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture , 2012 .
[31] Ken Kobayashi,et al. Mixed integer quadratic optimization formulations for eliminating multicollinearity based on variance inflation factor , 2018, Journal of Global Optimization.
[32] William W. Hsieh,et al. Nonlinear multivariate and time series analysis by neural network methods , 2004 .
[33] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[34] Guokun Lai,et al. Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks , 2017, SIGIR.
[35] Jun Gao,et al. Multivariate time series prediction of lane changing behavior using deep neural network , 2018, Applied Intelligence.
[36] Sebastián Maldonado,et al. Hierarchical time series forecasting via Support Vector Regression in the European Travel Retail Industry , 2019, Expert Syst. Appl..
[37] Rajesh Piplani,et al. Forecasting aggregate demand: An analytical evaluation of top-down versus bottom-up forecasting in a production planning framework , 2009 .
[38] Guoqiang Peter Zhang,et al. Neural network forecasting for seasonal and trend time series , 2005, Eur. J. Oper. Res..
[39] D. Bertsimas,et al. Best Subset Selection via a Modern Optimization Lens , 2015, 1507.03133.
[40] Dimitris Bertsimas,et al. Sparse Regression: Scalable Algorithms and Empirical Performance , 2019, Statistical Science.
[41] Rob J. Hyndman,et al. A note on the validity of cross-validation for evaluating autoregressive time series prediction , 2018, Comput. Stat. Data Anal..
[42] Souhaib Ben Taieb,et al. Regularized Regression for Hierarchical Forecasting Without Unbiasedness Conditions , 2019, KDD.
[43] Qiang Yang,et al. An Overview of Multi-task Learning , 2018 .
[44] Rob J. Hyndman,et al. Optimal combination forecasts for hierarchical time series , 2011, Comput. Stat. Data Anal..
[45] T. Hastie,et al. Learning Interactions via Hierarchical Group-Lasso Regularization , 2015, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.
[46] William B. Nicholson,et al. VARX-L: Structured Regularization for Large Vector Autoregressions with Exogenous Variables , 2015, 1508.07497.
[47] Guoqiang Peter Zhang,et al. Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.
[48] Yu Zhang,et al. A Survey on Multi-Task Learning , 2017, IEEE Transactions on Knowledge and Data Engineering.
[49] Carlos Capistrán,et al. Multi-horizon inflation forecasts using disaggregated data , 2010 .
[50] Yiran Chen,et al. Learning Structured Sparsity in Deep Neural Networks , 2016, NIPS.