Tensor extrapolation: an adaptation to data sets with missing entries

[1]  Gianluca Bontempi,et al.  Factor-Based Framework for Multivariate and Multi-step-ahead Forecasting of Large Scale Time Series , 2021, Frontiers in Big Data.

[2]  A. Rosenblad Accuracy of automatic forecasting methods for univariate time series data: A case study predicting the results of the 2018 Swedish general election using decades-long data series , 2021, Communications in Statistics: Case Studies, Data Analysis and Applications.

[3]  Dimane Mpoeleng,et al.  A survey on missing data in machine learning , 2021, Journal of Big Data.

[4]  Mischa Schmidt,et al.  A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning , 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

[5]  Josef Schosser,et al.  Tensor extrapolation: Forecasting large-scale relational data , 2021, J. Oper. Res. Soc..

[6]  Annie Qu,et al.  Tensors in Statistics , 2021 .

[7]  Wesley M. Gifford,et al.  AutoAI-TS: AutoAI for Time Series Forecasting , 2021, SIGMOD Conference.

[8]  Matthew J. Schneider,et al.  The tensor auto‐regressive model , 2020 .

[9]  Michael Bohlke-Schneider,et al.  Criteria for Classifying Forecasting Methods , 2020, International Journal of Forecasting.

[10]  Robert Fildes,et al.  Retail forecasting: Research and practice , 2019 .

[11]  Syama Sundar Rangapuram,et al.  GluonTS: Probabilistic Time Series Models in Python , 2019, ArXiv.

[12]  Feng Li,et al.  GRATIS: GeneRAting TIme Series with diverse and controllable characteristics , 2019, Stat. Anal. Data Min..

[13]  Evangelos Spiliotis,et al.  The M4 Competition: Results, findings, conclusion and way forward , 2018, International Journal of Forecasting.

[14]  Peter Lugtig,et al.  Generating missing values for simulation purposes: a multivariate amputation procedure , 2018, Journal of Statistical Computation and Simulation.

[15]  Stephan Günnemann,et al.  Introduction to Tensor Decompositions and their Applications in Machine Learning , 2017, ArXiv.

[16]  D. Donoho 50 Years of Data Science , 2017 .

[17]  Valentin Flunkert,et al.  DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks , 2017, International Journal of Forecasting.

[18]  Maja Pantic,et al.  TensorLy: Tensor Learning in Python , 2016, J. Mach. Learn. Res..

[19]  Nikos D. Sidiropoulos,et al.  Tensors for Data Mining and Data Fusion , 2016, ACM Trans. Intell. Syst. Technol..

[20]  Michael D. Ward,et al.  A new approach to analyzing coevolving longitudinal networks in international relations , 2016 .

[21]  Rob Kitchin,et al.  What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets , 2016, Big Data Soc..

[22]  Jan vom Brocke,et al.  Utilizing big data analytics for information systems research: challenges, promises and guidelines , 2016, Eur. J. Inf. Syst..

[23]  Peter D. Hoff,et al.  MULTILINEAR TENSOR REGRESSION FOR LONGITUDINAL RELATIONAL DATA. , 2014, The annals of applied statistics.

[24]  Fotios Petropoulos,et al.  'Horses for Courses' in demand forecasting , 2014, Eur. J. Oper. Res..

[25]  R. Kitchin,et al.  Big Data, new epistemologies and paradigm shifts , 2014, Big Data Soc..

[26]  George Athanasopoulos,et al.  Forecasting: principles and practice , 2013 .

[27]  Yu He,et al.  Statistical Significance of the Netflix Challenge , 2012, 1207.5649.

[28]  Longbing Cao,et al.  New Frontiers in Applied Data Mining: PAKDD 2011 International Workshops , 2012 .

[29]  Sahin Albayrak,et al.  Link Prediction on Evolving Data Using Tensor Factorization , 2011, PAKDD Workshops.

[30]  Mark Liberman,et al.  Obituary: Fred Jelinek , 2010, CL.

[31]  Tamara G. Kolda,et al.  Temporal Link Prediction Using Matrix and Tensor Factorizations , 2010, TKDD.

[32]  S. Amari,et al.  Nonnegative Matrix and Tensor Factorizations - Applications to Exploratory Multi-way Data Analysis and Blind Source Separation , 2009 .

[33]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[34]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[35]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[36]  R. Bro,et al.  PARAFAC and missing values , 2005 .

[37]  Roderick J. A. Little,et al.  Statistical Analysis with Missing Data: Little/Statistical Analysis with Missing Data , 2002 .

[38]  Rob J Hyndman,et al.  A state space framework for automatic forecasting using exponential smoothing methods , 2002 .

[39]  H. Kiers Towards a standardized notation and terminology in multiway analysis , 2000 .

[40]  Robert Fildes,et al.  Evaluation of Aggregate and Individual Forecast Method Selection Rules , 1989 .

[41]  Subir Ghosh,et al.  Statistical Analysis With Missing Data , 1988 .

[42]  D. Rubin INFERENCE AND MISSING DATA , 1975 .

[43]  J. Chang,et al.  Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .

[44]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[45]  F. L. Hitchcock The Expression of a Tensor or a Polyadic as a Sum of Products , 1927 .

[46]  Spyros Makridakis,et al.  The M5 Accuracy competition: Results, findings and conclusions , 2020 .

[47]  Josef Schosser,et al.  Multivariate Extrapolation: A Tensor-Based Approach , 2019, OR.

[48]  Skipper Seabold,et al.  Statsmodels: Econometric and Statistical Modeling with Python , 2010, SciPy.

[49]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[50]  S. Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[51]  Richard A. Harshman,et al.  Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .