Classical and Contemporary Approaches to Big Time Series Forecasting
暂无分享,去创建一个
Christos Faloutsos | Tim Januschowski | Jan Gasthaus | Yuyang Wang | C. Faloutsos | Bernie Wang | Jan Gasthaus | Tim Januschowski
[1] Chris Arney,et al. Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Easley, D. and Kleinberg, J.; 2010) [Book Review] , 2013, IEEE Technology and Society Magazine.
[2] Valentin Flunkert,et al. DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks , 2017, International Journal of Forecasting.
[3] Edward Y. Chang,et al. Adaptive stream resource management using Kalman Filters , 2004, SIGMOD '04.
[4] Marcus O'Connor,et al. Artificial neural network models for forecasting and decision making , 1994 .
[5] Le Song,et al. Wasserstein Learning of Deep Generative Point Process Models , 2017, NIPS.
[6] Robert Jenssen,et al. An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting , 2017, ArXiv.
[7] P. Young,et al. Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.
[8] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[9] Christos Faloutsos,et al. TensorCast: Forecasting with Context Using Coupled Tensors (Best Paper Award) , 2017, 2017 IEEE International Conference on Data Mining (ICDM).
[10] A. Raftery,et al. Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .
[11] Christos Faloutsos,et al. F4: large-scale automated forecasting using fractals , 2002, CIKM '02.
[12] Christos Faloutsos,et al. Prediction and indexing of moving objects with unknown motion patterns , 2004, SIGMOD '04.
[13] Hossein Shayeghi,et al. A Neural Network Based Short Term Load Forecasting , 2007, IC-AI.
[14] Evangelos Spiliotis,et al. Statistical and Machine Learning forecasting methods: Concerns and ways forward , 2018, PloS one.
[15] Uri Shalit,et al. Structured Inference Networks for Nonlinear State Space Models , 2016, AAAI.
[16] Michael Y. Hu,et al. Forecasting with artificial neural networks: The state of the art , 1997 .
[17] Cyrus Shahabi,et al. Graph Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting , 2017, ArXiv.
[18] Leo Razoumov,et al. Gini-regularized Optimal Transport with an Application to Spatio-Temporal Forecasting , 2017, ArXiv.
[19] Matthias W. Seeger,et al. Bayesian Intermittent Demand Forecasting for Large Inventories , 2016, NIPS.
[20] Philip S. Yu,et al. Optimal multi-scale patterns in time series streams , 2006, SIGMOD Conference.
[21] Heiga Zen,et al. WaveNet: A Generative Model for Raw Audio , 2016, SSW.
[22] Tamara G. Kolda,et al. Higher-order Web link analysis using multilinear algebra , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).
[23] P. Kaye. Infectious diseases of humans: Dynamics and control , 1993 .
[24] Christos Faloutsos,et al. Fast mining and forecasting of complex time-stamped events , 2012, KDD.
[25] Gwilym M. Jenkins,et al. Time series analysis, forecasting and control , 1971 .
[26] Christos Faloutsos,et al. DynaMMo: mining and summarization of coevolving sequences with missing values , 2009, KDD.
[27] Alex Smola,et al. Deep Factors with Gaussian Processes for Forecasting , 2018, ArXiv.
[28] George Athanasopoulos,et al. Forecasting: principles and practice , 2013 .
[29] Vitaly Kuznetsov,et al. Foundations of Sequence-to-Sequence Modeling for Time Series , 2018, AISTATS.
[30] E. Fox,et al. Achieving a Hyperlocal Housing Price Index: Overcoming Data Sparsity by Bayesian Dynamical Modeling of Multiple Data Streams , 2015, 1505.01164.
[31] Dennis Shasha,et al. StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time , 2002, VLDB.
[32] Gautier Marti,et al. Autoregressive Convolutional Neural Networks for Asynchronous Time Series , 2017, ICML.
[33] Zachary C. Lipton,et al. Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals , 2017, ArXiv.
[34] Matthias W. Seeger,et al. Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale , 2017, ArXiv.
[35] Rob J Hyndman,et al. Forecasting with Exponential Smoothing: The State Space Approach , 2008 .
[36] K. Torkkola,et al. A Multi-Horizon Quantile Recurrent Forecaster , 2017, 1711.11053.
[37] Matthias W. Seeger,et al. Deep State Space Models for Time Series Forecasting , 2018, NeurIPS.
[38] Christos Faloutsos,et al. TensorCast: Forecasting Time-Evolving Networks with Contextual Information , 2018, IJCAI.
[39] Christos Faloutsos,et al. Finding patterns in blog shapes and blog evolution , 2007, ICWSM.
[40] Steven L. Scott,et al. Predicting the Present with Bayesian Structural Time Series , 2013, Int. J. Math. Model. Numer. Optimisation.
[41] Rob J. Hyndman,et al. Forecasting with Exponential Smoothing , 2008 .
[42] Robert J. Marks,et al. Electric load forecasting using an artificial neural network , 1991 .
[43] Siew Lan Loo. Neural networks for financial forecasting , 1994 .
[44] Joos-Hendrik Böse,et al. Probabilistic Demand Forecasting at Scale , 2017, Proc. VLDB Endow..
[45] Melvin J. Hinich,et al. Time Series Analysis by State Space Methods , 2001 .
[46] Albert-László Barabási,et al. The origin of bursts and heavy tails in human dynamics , 2005, Nature.
[47] Frank M. Bass,et al. A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..
[48] Christos Faloutsos,et al. Ecosystem on the Web: non-linear mining and forecasting of co-evolving online activities , 2016, World Wide Web.
[49] Christos Faloutsos,et al. Adaptive, Hands-Off Stream Mining , 2003, VLDB.
[50] A. Raftery,et al. Probabilistic forecasts, calibration and sharpness , 2007 .
[51] Hyun Ah Song,et al. PowerCast: Mining and Forecasting Power Grid Sequences , 2017, ECML/PKDD.
[52] Jure Leskovec,et al. Meme-tracking and the dynamics of the news cycle , 2009, KDD.
[53] Christos Faloutsos,et al. BeatLex: Summarizing and Forecasting Time Series with Patterns , 2017, ECML/PKDD.
[54] Dit-Yan Yeung,et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.
[55] Yisong Yue,et al. Long-term Forecasting using Tensor-Train RNNs , 2017, ArXiv.
[56] Sunita Sarawagi,et al. ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting , 2018, ArXiv.
[57] Tamara G. Kolda,et al. Tensor Decompositions and Applications , 2009, SIAM Rev..
[58] Syama Sundar Rangapuram,et al. Deep Learning for Forecasting: Current Trends and Challenges , 2018 .
[59] Christos Faloutsos,et al. Online data mining for co-evolving time sequences , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).
[60] Andrew Harvey,et al. Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .
[61] Syama Sundar Rangapuram,et al. Deep Learning for Forecasting , 2018 .
[62] R. Axelrod,et al. Evolutionary Dynamics , 2004 .
[63] Christos Faloutsos,et al. Rise and fall patterns of information diffusion: model and implications , 2012, KDD.
[64] Jimeng Sun,et al. Beyond streams and graphs: dynamic tensor analysis , 2006, KDD '06.
[65] Christos Faloutsos,et al. TensorCast : Forecasting with Context using Coupled Tensors , 2017 .
[66] Christos Faloutsos,et al. Parsimonious linear fingerprinting for time series , 2010, Proc. VLDB Endow..
[67] Richard A. Davis,et al. Time Series: Theory and Methods , 2013 .
[68] Koh Takeuchi,et al. Autoregressive Tensor Factorization for Spatio-Temporal Predictions , 2017, 2017 IEEE International Conference on Data Mining (ICDM).
[69] Christos Faloutsos,et al. Forecasting Big Time Series: Old and New , 2018, Proc. VLDB Endow..
[70] Robert M. May,et al. Theoretical Ecology: Principles and Applications , 1981 .
[71] Yu Zheng,et al. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.
[72] Hyun Ah Song,et al. StreamCast: Fast and Online Mining of Power Grid Time Sequences , 2018, SDM.
[73] Syama Sundar Rangapuram,et al. Probabilistic Forecasting with Spline Quantile Function RNNs , 2019, AISTATS.
[74] Ugur Demiryurek,et al. Latent Space Model for Road Networks to Predict Time-Varying Traffic , 2016, KDD.
[75] Christos Faloutsos,et al. FUNNEL: automatic mining of spatially coevolving epidemics , 2014, KDD.
[76] Utkarsh Upadhyay,et al. Recurrent Marked Temporal Point Processes: Embedding Event History to Vector , 2016, KDD.
[77] Christos Faloutsos,et al. Non-Linear Mining of Competing Local Activities , 2016, WWW.
[78] Ole Winther,et al. A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning , 2017, NIPS.
[79] Inderjit S. Dhillon,et al. Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction , 2016, NIPS.