@article{sathishkumar,
  author = {Sathishkumar V E and Yongyun Cho},
  title = {A rule-based model for Seoul Bike sharing demand prediction using weather data},
  journal = {European Journal of Remote Sensing},
  volume = {53},
  number = {sup1},
  pages = {166-183},
  year  = {2020},
  publisher = {Taylor & Francis},
  doi = {10.1080/22797254.2020.1725789},
  URL = {https://doi.org/10.1080/22797254.2020.1725789},
  eprint = {https://doi.org/10.1080/22797254.2020.1725789}
}

@article{schuijbroek,
  title = {Inventory rebalancing and vehicle routing in bike sharing systems},
  author = {Schuijbroek, J. and Hampshire, R.C. and van Hoeve, W.-J.},
  year = {2017},
  journal = {European Journal of Operational Research},
  volume = {257},
  number = {3},
  pages = {992-1004},
  url = {https://EconPapers.repec.org/RePEc:eee:ejores:v:257:y:2017:i:3:p:992-1004}
}

@online{bikedata,
  title={Seoul Bike Sharing Demand Data Set},
  author={UCI},
  place={Irvine, California},
  url={https://archive.ics.uci.edu/ml/datasets/Seoul+Bike+Sharing+Demand},
  year={2020},
  month={Mar}
}

@InProceedings{gao,
  author={Gao, Chang and Chen, Yong},
  editor={Stienmetz, Jason L. and Ferrer-Rosell, Berta and Massimo, David},
  title={Using Machine Learning Methods to Predict Demand for Bike Sharing},
  booktitle={Information and Communication Technologies in Tourism 2022},
  year={2022},
  publisher={Springer International Publishing},
  address={Cham},
  pages={282--296},
  isbn={978-3-030-94751-4}
}

@article{akin,
title = {A novel approach to model selection in tourism demand modeling},
journal = {Tourism Management},
volume = {48},
pages = {64-72},
year = {2015},
issn = {0261-5177},
doi = {https://doi.org/10.1016/j.tourman.2014.11.004},
url = {https://www.sciencedirect.com/science/article/pii/S026151771400226X},
author = {Melda Akin}
}

@article{kpss,
  title = {Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?},
  journal = {Journal of Econometrics},
  volume = {54},
  number = {1},
  pages = {159-178},
  year = {1992},
  issn = {0304-4076},
  doi = {https://doi.org/10.1016/0304-4076(92)90104-Y},
  url = {https://www.sciencedirect.com/science/article/pii/030440769290104Y},
  author = {Denis Kwiatkowski and Peter C.B. Phillips and Peter Schmidt and Yongcheol Shin}
}

@article{ljung,
    author = {LJUNG, G. M. and BOX, G. E. P.},
    title = "{On a measure of lack of fit in time series models}",
    journal = {Biometrika},
    volume = {65},
    number = {2},
    pages = {297-303},
    year = {1978},
    month = {08},
    issn = {0006-3444},
    doi = {10.1093/biomet/65.2.297},
    url = {https://doi.org/10.1093/biomet/65.2.297},
    eprint = {https://academic.oup.com/biomet/article-pdf/65/2/297/649058/65-2-297.pdf},
}

@book{fpp3,
    title = {Forecasting: Principles and Practice},
    author = {Rob Hyndman and G. Athanasopoulos},
    year = {2021},
    language = {English},
    publisher = {OTexts},
    address = {Melbourne, Australia},
    url = {OTexts.com/fpp3},
    edition = {3rd},
    urldate={6/26/2022}
}

@article{fcacc,
    title = {Another look at measures of forecast accuracy},
    journal = {International Journal of Forecasting},
    volume = {22},
    number = {4},
    pages = {679-688},
    year = {2006},
    issn = {0169-2070},
    doi = {https://doi.org/10.1016/j.ijforecast.2006.03.001},
    url = {https://www.sciencedirect.com/science/article/pii/S0169207006000239},
    author = {Rob J. Hyndman and Anne B. Koehler}
}

@article{boxcox,
 ISSN = {00359246},
 URL = {http://www.jstor.org/stable/2984418},
 author = {G. E. P. Box and D. R. Cox},
 journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
 number = {2},
 pages = {211--252},
 publisher = {[Royal Statistical Society, Wiley]},
 title = {An Analysis of Transformations},
 urldate = {2022-06-29},
 volume = {26},
 year = {1964}
}

@article{guerrero,
author = {Guerrero, Victor M.},
title = {Time-series analysis supported by power transformations},
journal = {Journal of Forecasting},
volume = {12},
number = {1},
pages = {37-48},
keywords = {ARIMA models, Bias reduction, Forecasting, Taylor series approximation, Time-series models, Variance-stabilizing},
doi = {https://doi.org/10.1002/for.3980120104},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/for.3980120104},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/for.3980120104},
year = {1993}
}

@article{autoarima,
 title={Automatic Time Series Forecasting: The forecast Package for R},
 volume={27},
 url={https://www.jstatsoft.org/index.php/jss/article/view/v027i03},
 doi={10.18637/jss.v027.i03},
 number={3},
 journal={Journal of Statistical Software},
 author={Hyndman, Rob J. and Khandakar, Yeasmin},
 year={2008},
 pages={1–22}
}

@Manual{fable,
  title = {{fable}: Forecasting Models for Tidy Time Series},
  author =  {Mitchell O'Hara-Wild and Rob Hyndman and Earo Wang},
  year = {2021},
  note = {R package version 0.3.1},
  url = {https://fable.tidyverts.org},
}

@book{shumway,
  title={Time Series: A Data Analysis Approach Using R},
  author={Shumway, R.H. and Stoffer, D.S.},
  isbn={9780367221096},
  lccn={2019018441},
  series={A Chapman \& Hall book},
  year={2019},
  publisher={CRC Press, Taylor \& Francis Group}
}

@article{prophet,
author = {Sean J. Taylor and Benjamin Letham},
title = {Forecasting at Scale},
journal = {The American Statistician},
volume = {72},
number = {1},
pages = {37-45},
year  = {2018},
publisher = {Taylor & Francis},
doi = {10.1080/00031305.2017.1380080},
URL = {https://doi.org/10.1080/00031305.2017.1380080},
eprint = {https://doi.org/10.1080/00031305.2017.1380080}
}

@Manual{fableprophet,
  title = {{fable.prophet}: Prophet Modelling Interface for 'fable'},
  author =  {Mitchell O'Hara-Wild},
  year = {2020},
  note = {R package version 0.1.0},
  url = {https://fable.tidyverts.org},
}

@Manual{fasster,
  title = {{fasster}: Fast Additive Switching of Seasonality, Trend and Exogenous Regressors},
  author =  {Mitchell O'Hara-Wild and Rob Hyndman},
  year = {2022},
  note = {R package version 0.1.0.9100},
  url = {https://github.com/mitchelloharawild/fasster},
}

@article{JMLRdelgado14a,
  author  = {Manuel Fern{{\'a}}ndez-Delgado and Eva Cernadas and Sen{{\'e}}n Barro and Dinani Amorim},
  title   = {Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?},
  journal = {Journal of Machine Learning Research},
  year    = {2014},
  volume  = {15},
  number  = {90},
  pages   = {3133--3181},
  url     = {http://jmlr.org/papers/v15/delgado14a.html}
}

@article{probst,
author = {Probst, Philipp and Wright, Marvin N. and Boulesteix, Anne-Laure},
title = {Hyperparameters and tuning strategies for random forest},
journal = {WIREs Data Mining and Knowledge Discovery},
volume = {9},
number = {3},
pages = {e1301},
keywords = {ensemble, literature review, out-of-bag, performance evaluation, ranger, sequential model-based optimization, tuning parameter},
doi = {https://doi.org/10.1002/widm.1301},
url = {https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/widm.1301},
eprint = {https://wires.onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1301},
year = {2019}
}

@online{banerjee,
	author = {Prashant Banerjee},
	title = {A Guide on XGBoost Hyperparameters Tuning},
	date = {2020},
	url = {https://www.kaggle.com/code/prashant111/a-guide-on-xgboost-hyperparameters-tuning/notebook},
	organization = {Kaggle}
}

@article{hochreiter_1997_long,
  author = {Hochreiter, Sepp and Schmidhuber, Jürgen},
  month = {11},
  pages = {1735-1780},
  title = {Long Short-Term Memory},
  doi = {10.1162/neco.1997.9.8.1735},
  volume = {9},
  year = {1997},
  journal = {Neural Computation}
}

@article{mujeeb_2019_deep,
  author = {Mujeeb, Sana and Javaid, Nadeem and Ilahi, Manzoor and Wadud, Zahid and Ishmanov, Farruh and Afzal, Muhammad},
  month = {02},
  pages = {987},
  title = {Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities},
  doi = {10.3390/su11040987},
  urldate = {2020-04-29},
  volume = {11},
  year = {2019},
  journal = {Sustainability}
}

@misc{httpswwwfacebookcomjasonbrownlee39_2018_multivariate,
  author = {Brownlee, Jason},
  month = {09},
  title = {Multivariate Time Series Forecasting with LSTMs in Keras},
  url = {https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/},
  year = {2018},
  organization = {Machine Learning Mastery}
}

@misc{miguel_2021_how,
  author = {Miguel, Tiago},
  month = {01},
  title = {How the LSTM improves the RNN},
  url = {https://towardsdatascience.com/how-the-lstm-improves-the-rnn-1ef156b75121},
  year = {2021},
  organization = {Medium}
}

@misc{brownlee_2020_how,
  author = {Brownlee, Jason},
  month = {06},
  title = {How to Use StandardScaler and MinMaxScaler Transforms in Python},
  url = {https://machinelearningmastery.com/standardscaler-and-minmaxscaler-transforms-in-python/},
  year = {2020},
  organization = {Machine Learning Mastery}
}