暂无分享,去创建一个
[1] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[2] Rob J. Hyndman,et al. FFORMA: Feature-based forecast model averaging , 2020, International Journal of Forecasting.
[3] Christoph Bergmeir,et al. Global Models for Time Series Forecasting: A Simulation Study , 2020, ArXiv.
[4] J. M. Bates,et al. The Combination of Forecasts , 1969 .
[5] Luís Torgo,et al. Dynamic and Heterogeneous Ensembles for Time Series Forecasting , 2017, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA).
[6] Kevin Leyton-Brown,et al. Sequential Model-Based Optimization for General Algorithm Configuration , 2011, LION.
[7] Robert E. Schapire,et al. A Brief Introduction to Boosting , 1999, IJCAI.
[8] Rob J Hyndman,et al. Another look at measures of forecast accuracy , 2006 .
[9] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[10] Juan R. Trapero,et al. On the identification of sales forecasting models in the presence of promotions , 2015, J. Oper. Res. Soc..
[11] Evangelos Spiliotis,et al. The M4 Competition: Results, findings, conclusion and way forward , 2018, International Journal of Forecasting.
[12] A. Timmermann. Forecast Combinations , 2005 .
[13] Peter J. Rousseeuw,et al. Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .
[14] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[15] Peibei Shi,et al. Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology , 2019, ICONIP.
[16] Robert L. Winkler,et al. Simple robust averages of forecasts: Some empirical results , 2008 .
[17] Xiaozhe Wang,et al. Characteristic-Based Clustering for Time Series Data , 2006, Data Mining and Knowledge Discovery.
[18] Rob J. Hyndman,et al. Large-Scale Unusual Time Series Detection , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).
[19] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[20] Luís Torgo,et al. Ensembles for Time Series Forecasting , 2014, ACML.
[21] Christoph Bergmeir,et al. Towards Accurate Predictions and Causal ‘What-if’ Analyses for Planning and Policy-making: A Case Study in Emergency Medical Services Demand , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).
[22] Hans-Georg Zimmermann,et al. Recurrent Neural Networks Are Universal Approximators , 2006, ICANN.
[23] Valentin Flunkert,et al. DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks , 2017, International Journal of Forecasting.
[24] Slawek Smyl,et al. A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting , 2020, International Journal of Forecasting.
[25] Casper Solheim Bojer,et al. Kaggle forecasting competitions: An overlooked learning opportunity , 2020, ArXiv.
[26] Alexis Sardá-Espinosa,et al. Time-Series Clustering in R Using the dtwclust Package , 2019, R J..
[27] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[28] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.
[29] Francesca Mangili,et al. Should We Really Use Post-Hoc Tests Based on Mean-Ranks? , 2015, J. Mach. Learn. Res..
[30] Andrew W. Moore,et al. X-means: Extending K-means with Efficient Estimation of the Number of Clusters , 2000, ICML.
[31] Adrian E. Raftery,et al. Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .
[32] T. Warren Liao,et al. Clustering of time series data - a survey , 2005, Pattern Recognit..
[33] David L. Dowe,et al. Intrinsic classification by MML - the Snob program , 1994 .
[34] Leandro dos Santos Coelho,et al. Multi-Objective Ensemble Model for Short-Term Price Forecasting in Corn Price Time Series , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).
[35] Ismael Sánchez,et al. Adaptive combination of forecasts with application to wind energy , 2008 .
[36] Hsiang-Fu Yu,et al. Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting , 2019, NeurIPS.
[37] P. Young,et al. Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.
[38] Rob J Hyndman,et al. Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing , 2011 .
[39] Rob J Hyndman,et al. Forecasting with Exponential Smoothing: The State Space Approach , 2008 .
[40] Pablo Montero-Manso,et al. Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality , 2020, ArXiv.
[41] Agata Chorowska,et al. Weighted ensemble of statistical models , 2018, 1811.07761.
[42] George Athanasopoulos,et al. Forecasting: principles and practice , 2013 .
[43] Anna Bou Ezzeddine,et al. Incremental Ensemble Learning for Electricity Load Forecasting , 2016 .
[44] M. Friedman. A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings , 1940 .
[45] Oliver Kramer,et al. Machine learning ensembles for wind power prediction , 2016 .
[46] Irma J. Terpenning,et al. STL : A Seasonal-Trend Decomposition Procedure Based on Loess , 1990 .
[47] Rob J. Hyndman,et al. Forecasting with Exponential Smoothing , 2008 .
[48] Germain Forestier,et al. Deep learning for time series classification: a review , 2018, Data Mining and Knowledge Discovery.
[49] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[50] Spyros Makridakis,et al. The M3-Competition: results, conclusions and implications , 2000 .
[51] Christoph Bergmeir,et al. Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions , 2019, ArXiv.
[52] Martin Stepnicka,et al. On the results and observations of the time series forecasting competition CIF 2016 , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[53] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[54] P. Rousseeuw,et al. Partitioning Around Medoids (Program PAM) , 2008 .
[55] Gábor Petneházi,et al. Recurrent Neural Networks for Time Series Forecasting , 2018, ArXiv.
[56] Geoffrey I. Webb,et al. A Strong Baseline for Weekly Time Series Forecasting , 2020, ArXiv.
[57] Kurt Hornik,et al. Open-source machine learning: R meets Weka , 2009, Comput. Stat..
[58] Tatiana Tommasi,et al. Training Deep Networks without Learning Rates Through Coin Betting , 2017, NIPS.
[59] Christoph Bergmeir,et al. LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns , 2020, IEEE transactions on neural networks and learning systems.
[60] Christoph Bergmeir,et al. Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach , 2017, Expert Syst. Appl..
[61] Geoffrey I. Webb,et al. Encyclopedia of Machine Learning , 2011, Encyclopedia of Machine Learning.
[62] Arnaud Prouzeau,et al. Simulation and Optimisation of Air Conditioning Systems using Machine Learning , 2020, ArXiv.
[63] Fotios Petropoulos,et al. forecast: Forecasting functions for time series and linear models , 2018 .
[64] Michael Bohlke-Schneider,et al. Criteria for Classifying Forecasting Methods , 2020, International Journal of Forecasting.
[65] Nicolas Chapados,et al. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting , 2019, ICLR.
[66] Rob J Hyndman,et al. Automatic Time Series Forecasting: The forecast Package for R , 2008 .
[67] Leo Breiman,et al. Random Forests , 2001, Machine Learning.