reference.bib

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@INPROCEEDINGS{Grecov2021-ws,
   title     = "Causal Inference Using Global Forecasting Models for
                Counterfactual Prediction",
   booktitle = "Advances in Knowledge Discovery and Data Mining",
   author    = "Grecov, Priscila and Bandara, Kasun and Bergmeir, Christoph and
                Ackermann, Klaus and Campbell, Sam and Scott, Deborah and
                Lubman, Dan",
   publisher = "Springer International Publishing",
   pages     = "282-294",
   year      =  2021
 }

@MISC{Bergmeir2020-nu,
   title        = "{ACML} 2020 Tutorial: Forecasting for Data Scientists",
   author       = "Bergmeir, C",
   year         =  2020,
   howpublished = "\url{https://cbergmeir.com/talks/acml-tutorial/}",
   note         = "Accessed: 2021-8-29"
}


@ARTICLE{Godahewa2020-wg,
   title    = "A Strong Baseline for Weekly Time Series Forecasting",
   author   = "Godahewa, Rakshitha and Bergmeir, C and Webb, Geoffrey I and
               Montero-Manso, Pablo",
   journal  = "ArXiv",
   year     =  2020
}

 @ARTICLE{Lim2021-il,
   title   = "Temporal Fusion Transformers for interpretable multi-horizon time
              series forecasting",
   author  = "Lim, Bryan and Ar{\i}k, Sercan {\"O} and Loeff, Nicolas and
              Pfister, Tomas",
   journal = "Int. J. Forecast.",
   month   =  jun,
   year    =  2021
 }


@INPROCEEDINGS{Li2019-io,
   title     = "Enhancing the Locality and Breaking the Memory Bottleneck of
                Transformer on Time Series Forecasting",
   booktitle = "Advances in Neural Information Processing Systems",
   author    = "Li, Shiyang and Jin, Xiaoyong and Xuan, Yao and Zhou, Xiyou and
                Chen, Wenhu and Wang, Yu-Xiang and Yan, Xifeng",
   editor    = "Wallach, H and Larochelle, H and Beygelzimer, A",
   publisher = "Curran Associates, Inc.",
   volume    =  32,
   year      =  2019
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@ARTICLE{Oreshkin2019-tq,
   title         = "{N-BEATS}: Neural basis expansion analysis for interpretable
                    time series forecasting",
   author        = "Oreshkin, Boris N and Carpov, Dmitri and Chapados, Nicolas
                    and Bengio, Yoshua",
   month         =  may,
   year          =  2019,
   archivePrefix = "arXiv",
   primaryClass  = "cs.LG",
   eprint        = "1905.10437"
}


@INPROCEEDINGS{Rangapuram2018-zq,
   title     = "Deep State Space Models for Time Series Forecasting",
   booktitle = "Advances in Neural Information Processing Systems",
   author    = "Rangapuram, Syama Sundar and Seeger, Matthias W and Gasthaus,
                Jan and Stella, Lorenzo and Wang, Yuyang and Januschowski, Tim",
   editor    = "Bengio, S and Wallach, H and Larochelle, H and Grauman, K and
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   volume    =  31,
   year      =  2018
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@INPROCEEDINGS{Sen2019-jj,
   title     = "Think Globally, Act Locally: A Deep Neural Network Approach to
                {High-Dimensional} Time Series Forecasting",
   booktitle = "Advances in Neural Information Processing Systems",
   author    = "Sen, Rajat and Yu, Hsiang-Fu and Dhillon, Inderjit S",
   editor    = "Wallach, H and Larochelle, H and Beygelzimer",
   publisher = "Curran Associates, Inc.",
   volume    =  32,
   year      =  2019
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@ARTICLE{Salinas2020-dz,
   title   = "{DeepAR}: Probabilistic forecasting with autoregressive recurrent
              networks",
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              Januschowski, Tim",
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@ARTICLE{Hewamalage2021-hg,
   title   = "Recurrent Neural Networks for Time Series Forecasting: Current
              status and future directions",
   author  = "Hewamalage, Hansika and Bergmeir, Christoph and Bandara, Kasun",
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   month   =  jan,
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@ARTICLE{Makridakis2021-ro,
   title     = "The {M5} Competition and the Future of Human Expertise in
                Forecasting",
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   journal   = "Foresight: The International Journal of Applied Forecasting",
   publisher = "International Institute of Forecasters",
   number    =  60,
   pages     = "33-37",
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@MISC{Suilin2018-tc,
   title        = "kaggle-web-traffic",
   author       = "Suilin, A",
   year         =  2018,
   howpublished = "\url{https://github.com/Arturus/kaggle-web-traffic}",
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@ARTICLE{Montero-Manso2021-es,
   title   = "Principles and algorithms for forecasting groups of time series:
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   month   =  jun,
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@MANUAL{R_Core_Team2013-bo,
  title        = "R: A Language and Environment for Statistical Computing",
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  address      = "Vienna, Austria",
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@INPROCEEDINGS{He2016-wm,
  title     = "Deep Residual Learning for Image Recognition",
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               Recognition ({CVPR})",
  author    = "He, K and Zhang, X and Ren, S and Sun, J",
  pages     = "770-778",
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@ARTICLE{Fawaz2018-dq,
  title         = "Data augmentation using synthetic data for time series
                   classification with deep residual networks",
  author        = "Fawaz, Hassan Ismail and Forestier, Germain and Weber,
                   Jonathan and Idoumghar, Lhassane and Muller, Pierre-Alain",
  month         =  aug,
  year          =  2018,
  archivePrefix = "arXiv",
  primaryClass  = "cs.CV",
  eprint        = "1808.02455"
}

@ARTICLE{Kang2019-dy,
  title         = "{GRATIS}: {GeneRAting} {TIme} Series with diverse and
                   controllable characteristics",
  author        = "Kang, Yanfei and Hyndman, Rob J and Li, Feng",
  month         =  mar,
  year          =  2019,
  archivePrefix = "arXiv",
  primaryClass  = "stat.ML",
  eprint        = "1903.02787"
}

@ARTICLE{Almonacid2013-ot,
  title   = "Generation of ambient temperature hourly time series for some
             Spanish locations by artificial neural networks",
  author  = "Almonacid, F and P{\'e}rez-Higueras, P and Rodrigo, P and
             Hontoria, L",
  journal = "Renewable Energy",
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  pages   = "285-291",
  month   =  mar,
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@INPROCEEDINGS{Iftikhar2017-wy,
  title     = "A Scalable Smart Meter Data Generator Using Spark",
  booktitle = "On the Move to Meaningful Internet Systems. {OTM} 2017
               Conferences",
  author    = "Iftikhar, Nadeem and Liu, Xiufeng and Danalachi, Sergiu and
               Nordbjerg, Finn Ebertsen and Vollesen, Jens Henrik",
  publisher = "Springer International Publishing",
  pages     = "21-36",
  year      =  2017
}

@INPROCEEDINGS{Kegel2018-rs,
  title           = "Feature-based comparison and generation of time series",
  booktitle       = "Proceedings of the 30th International Conference on
                     Scientific and Statistical Database Management - {SSDBM}
                     '18",
  author          = "Kegel, Lars and Hahmann, Martin and Lehner, Wolfgang",
  publisher       = "ACM Press",
  pages           = "1-12",
  year            =  2018,
  address         = "New York, New York, USA",
  conference      = "the 30th International Conference",
  location        = "Bozen-Bolzano, Italy"
}

@ARTICLE{Hewamalage2019-ko,
  title     = "Recurrent neural networks for time series forecasting: Current
               status and future directions",
  author    = "Hewamalage, H and Bergmeir, C and Bandara, K",
  journal   = "arXiv preprint arXiv:1909.00590",
  publisher = "arxiv.org",
  year      =  2019
}

@INPROCEEDINGS{Bandara2015-jn,
  title     = "Towards optimising {Wi-Fi} energy consumption in mobile phones:
               A data driven approach",
  booktitle = "2015 Fifteenth International Conference on Advances in {ICT} for
               Emerging Regions ({ICTer})",
  author    = "Bandara, H M K G and Caldera, H A",
  pages     = "226-235",
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  year      =  2015
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@ARTICLE{Seth2007-on,
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@ARTICLE{Dan_I_Lubman_Sharon_Matthews_Cherie_Heilbronn_Jessica_J_Killian_Rowan_P_Ogeil_Belinda_Lloyd_Katrina_Witt_Rose_CrossinKaren_Smith_Emma_Bosley_Rosemary_Carney_Alex_Wilson_Matthew_Eastham_Toby_Keene_Carol_Shipp_Debbie_Scott2020-nw,
  title   = "The National Ambulance Surveillance System: A novel method
             surveillance system for monitoring acute alcohol, illicit and
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             clinical records",
  author  = "{Dan I. Lubman, Sharon Matthews, Cherie Heilbronn, Jessica J.
             Killian Rowan P. Ogeil, Belinda Lloyd, Katrina Witt, Rose
             Crossin,Karen Smith, Emma Bosley, Rosemary Carney, Alex Wilson,
             Matthew Eastham, Toby Keene, Carol Shipp, Debbie Scott}",
  journal = "PLoS One",
  year    =  2020
}

@ARTICLE{Bandara2019-si,
  title     = "{LSTM-MSNet}: Leveraging Forecasts on Sets of Related Time
               Series with Multiple Seasonal Patterns",
  author    = "Bandara, K and Bergmeir, C and Hewamalage, H",
  journal   = "arXiv preprint arXiv:1909.04293",
  publisher = "arxiv.org",
  year      =  2019
}

@ARTICLE{Granger1980-om,
  title   = "{AN} {INTRODUCTION} {TO} {LONG-MEMORY} {TIME} {SERIES} {MODELS}
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  title     = "Measuring and testing Granger causality over the spectrum: An
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@ARTICLE{Zanias1994-wy,
  title     = "The long run, causality, and forecasting in the
               advertising-sales relationship",
  author    = "Zanias, George P",
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@ARTICLE{Narayan2005-kq,
  title     = "Electricity consumption, employment and real income in Australia
               evidence from multivariate Granger causality tests",
  author    = "Narayan, Paresh Kumar and Smyth, Russell",
  journal   = "Energy Policy",
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  volume    =  33,
  number    =  9,
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@INPROCEEDINGS{Bandara2019-bg,
  title     = "Sales Demand Forecast in E-commerce Using a Long {Short-Term}
               Memory Neural Network Methodology",
  booktitle = "Neural Information Processing",
  author    = "Bandara, Kasun and Shi, Peibei and Bergmeir, Christoph and
               Hewamalage, Hansika and Tran, Quoc and Seaman, Brian",
  publisher = "Springer International Publishing",
  pages     = "462-474",
  year      =  2019
}

@INPROCEEDINGS{Zhou2015-cx,
  title     = "Predicting Ambulance Demand: A {Spatio-Temporal} Kernel Approach",
  booktitle = "Proceedings of the 21th {ACM} {SIGKDD} International Conference
               on Knowledge Discovery and Data Mining",
  author    = "Zhou, Zhengyi and Matteson, David S",
  publisher = "ACM",
  pages     = "2297-2303",
  series    = "KDD '15",
  year      =  2015,
  address   = "New York, NY, USA",
  location  = "Sydney, NSW, Australia"
}

@PHDTHESIS{Zhou2015-sa,
  title    = "Predicting Ambulance Demand",
  author   = "Zhou, Zhengyi",
  month    =  aug,
  year     =  2015
}

@ARTICLE{Zhou2015-ic,
  title     = "A {Spatio-Temporal} Point Process Model for Ambulance Demand",
  author    = "Zhou, Zhengyi and Matteson, David S and Woodard, Dawn B and
               Henderson, Shane G and Micheas, Athanasios C",
  journal   = "J. Am. Stat. Assoc.",
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  volume    =  110,
  number    =  509,
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}

@ARTICLE{Matteson2011-zn,
  title     = "Forecasting emergency medical service call arrival rates",
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               Henderson, Shane G",
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@ARTICLE{Wong2014-ok,
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  title    = "A multivariate time series approach to modeling and forecasting
              demand in the emergency department",
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@ARTICLE{Grekousis2019-bs,
  title   = "Where will the next emergency event occur? Predicting ambulance
             demand in emergency medical services using artificial intelligence",
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@ARTICLE{Cadigan1989-sm,
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@ARTICLE{Vile2012-bq,
  title   = "Predicting ambulance demand using singular spectrum analysis",
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@ARTICLE{Chen2016-ka,
  title   = "Demand Forecast Using Data Analytics for the Preallocation of
             Ambulances",
  author  = "Chen, A Y and Lu, T and Ma, M H and Sun, W",
  journal = "IEEE Journal of Biomedical and Health Informatics",
  volume  =  20,
  number  =  4,
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@INPROCEEDINGS{Steins2019-eu,
  title     = "Forecasting the Demand for Emergency Medical Services",
  booktitle = "the 52nd Hawaii International Conference on System Sciences,
               2019, January 8-11, Grand Wailea, Maui, Hawai'i",
  author    = "Steins, Krisjanis and Matinrad, Niki and Andersson Granberg,
               Tobias",
  publisher = "University of Hawai'i at Manoa",
  pages     = "1855-1864",
  year      =  2019,
  language  = "en"
}

@ARTICLE{Channouf2007-vw,
  title    = "The application of forecasting techniques to modeling emergency
              medical system calls in Calgary, Alberta",
  author   = "Channouf, Nabil and Ecuyer, Pierre and Ingolfsson, Armann and
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  journal  = "Health Care Manag. Sci.",
  volume   =  10,
  number   =  1,
  pages    = "25-45",
  month    =  feb,
  year     =  2007,
  language = "en"
}

@ARTICLE{Matteson2011-lv,
  title         = "Forecasting emergency medical service call arrival rates",
  author        = "Matteson, David S and McLean, Mathew W and Woodard, Dawn B
                   and Henderson, Shane G",
  month         =  jul,
  year          =  2011,
  archivePrefix = "arXiv",
  primaryClass  = "stat.AP",
  eprint        = "1107.4919"
}

@ARTICLE{Zhou2015-hq,
  title         = "Predicting Melbourne Ambulance Demand using Kernel Warping",
  author        = "Zhou, Zhengyi and Matteson, David S",
  month         =  jul,
  year          =  2015,
  archivePrefix = "arXiv",
  primaryClass  = "stat.AP",
  eprint        = "1507.00363"
}

@ARTICLE{Zhou2015-kw,
  title         = "Predicting Ambulance Demand: a {Spatio-Temporal} Kernel
                   Approach",
  author        = "Zhou, Zhengyi and Matteson, David S",
  month         =  jul,
  year          =  2015,
  archivePrefix = "arXiv",
  primaryClass  = "stat.AP",
  eprint        = "1507.00364"
}

@ARTICLE{Zhou2016-ck,
  title         = "Predicting Ambulance Demand: Challenges and Methods",
  author        = "Zhou, Zhengyi",
  month         =  jun,
  year          =  2016,
  archivePrefix = "arXiv",
  primaryClass  = "stat.ML",
  eprint        = "1606.05363"
}

@ARTICLE{Walsh2017-dv,
  title     = "Predicting Risk of Suicide Attempts Over Time Through Machine
               Learning",
  author    = "Walsh, Colin G and Ribeiro, Jessica D and Franklin, Joseph C",
  journal   = "Clin. Psychol. Sci.",
  publisher = "SAGE Publications Inc",
  volume    =  5,
  number    =  3,
  pages     = "457-469",
  month     =  may,
  year      =  2017
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@INCOLLECTION{Cho2014-or,
  title     = "Learning phrase representations using {RNN} encoder-decoder for
               statistical machine translation",
  booktitle = "Conference on Empirical Methods in Natural Language Processing
               ({EMNLP} 2014)",
  author    = "Cho, Kyunghyun and van Merrienboer, B and Gulcehre, Caglar and
               Bougares, F and Schwenk, H and Bengio, Yoshua",
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@ARTICLE{Yu2015-hj,
  title         = "High-dimensional Time Series Prediction with Missing Values",
  author        = "Yu, Hsiang-Fu and Rao, Nikhil and Dhillon, Inderjit S",
  month         =  sep,
  year          =  2015,
  archivePrefix = "arXiv",
  primaryClass  = "cs.LG",
  eprint        = "1509.08333"
}

@ARTICLE{Fernando2018-cx,
  title   = "Tree Memory Networks for modelling long-term temporal dependencies",
  author  = "Fernando, Tharindu and Denman, Simon and McFadyen, Aaron and
             Sridharan, Sridha and Fookes, Clinton",
  journal = "Neurocomputing",
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  year    =  2018
}

@ARTICLE{Fernando2017-qc,
  title         = "Tree Memory Networks for Modelling Long-term Temporal
                   Dependencies",
  author        = "Fernando, Tharindu and Denman, Simon and McFadyen, Aaron and
                   Sridharan, Sridha and Fookes, Clinton",
  month         =  mar,
  year          =  2017,
  archivePrefix = "arXiv",
  primaryClass  = "cs.LG",
  eprint        = "1703.04706"
}

@ARTICLE{Du2013-db,
  title     = "Unsupervised transfer learning for target detection from
               hyperspectral images",
  author    = "Du, Bo and Zhang, Liangpei and Tao, Dacheng and Zhang, Dengyi",
  journal   = "Neurocomputing",
  publisher = "Elsevier",
  volume    =  120,
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@ARTICLE{Yu2017-dm,
  title     = "Deep Transfer Learning for Modality Classification of Medical
               Images",
  author    = "Yu, Yuhai and Lin, Hongfei and Meng, Jiana and Wei, Xiaocong and
               Guo, Hai and Zhao, Zhehuan",
  journal   = "Information",
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}

@ARTICLE{Yang2017-sp,
  title         = "Transfer Learning for Sequence Tagging with Hierarchical
                   Recurrent Networks",
  author        = "Yang, Zhilin and Salakhutdinov, Ruslan and Cohen, William W",
  month         =  mar,
  year          =  2017,
  archivePrefix = "arXiv",
  primaryClass  = "cs.CL",
  eprint        = "1703.06345"
}

@INPROCEEDINGS{Ramachandran2017-px,
  title     = "Unsupervised Pretraining for Sequence to Sequence Learning",
  booktitle = "Proceedings of the 2017 Conference on Empirical Methods in
               Natural Language Processing",
  author    = "Ramachandran, Prajit and Liu, Peter J and Le, Quoc",
  pages     = "383-391",
  year      =  2017
}

@ARTICLE{Shin2016-zd,
  title         = "Generative Knowledge Transfer for Neural Language Models",
  author        = "Shin, Sungho and Hwang, Kyuyeon and Sung, Wonyong",
  month         =  aug,
  year          =  2016,
  archivePrefix = "arXiv",
  primaryClass  = "cs.LG",
  eprint        = "1608.04077"
}

@INPROCEEDINGS{Bengio2012-tr,
  title     = "Deep Learning of Representations for Unsupervised and Transfer
               Learning",
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% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
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@MISC{Sverdlik2016-sa,
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@MISC{Smyl2016-ee,
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