References.bib

@Manual{scott_boom_2022,
    title = {Boom: Bayesian Object Oriented Modeling},
    author = {Steven L. Scott},
    year = {2022},
    note = {R package version 0.9.11},
    url = {https://CRAN.R-project.org/package=Boom},
  }


@article{li_2023,
  title={Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution},
  author={Li, Yan and Lu, Xinjiang and Xiong, Haoyi and Tang, Jian and Su, Jiantao and Jin, Bo and Dou, Dejing},
  journal={arXiv preprint arXiv:2301.02068},
  year={2023}
}

@INPROCEEDINGS{Makhloufi_2018,
  author={Makhloufi, Saida and Debbache, Mohammed and Boulahchiche, Saliha},
  booktitle={2018 International Conference on Wind Energy and Applications in Algeria (ICWEAA)}, 
  title={Long-term Forecasting of Intermittent Wind and Photovoltaic Resources by using Adaptive Neuro Fuzzy Inference System (ANFIS)}, 
  year={2018},
  volume={},
  number={},
  pages={1-4},
  doi={10.1109/ICWEAA.2018.8605102}}


@article{talebizadeh_2011,
title = {Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models},
journal = {Expert Systems with Applications},
volume = {38},
number = {4},
pages = {4126-4135},
year = {2011},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2010.09.075},
url = {https://www.sciencedirect.com/science/article/pii/S0957417410010328},
author = {Mansour Talebizadeh and Ali Moridnejad},
keywords = {Uncertainty analysis, Prediction intervals, Lake level, ANN, ANFIS, Bootstrapping},
abstract = {In this study various ANN and ANFIS models were developed to forecast the lake level fluctuations in Lake Urmia in northwest of Iran. In addition to the time series of lake levels, the time series of three most effective variables in the water budget of the lake namely, the rainfall, evaporation and inflow were also used to find the best input variables to the models. Furthermore the uncertainty due to the error in measuring the hydrological variables and also the uncertainty in the outputs of ANN and ANFIS models which stems from their sensitivity to the training sets used to train these models and also the initial configuration before commencement of training were estimated. Comparing the outputs and confidence intervals of the two types of models it was found that the results of ANFIS model are superior to those of ANN’ in that they are both more accurate and with less uncertainty.}
}

@Manual{scott_bsts_2022,
    title = {bsts: Bayesian Structural Time Series},
    author = {Steven L. Scott},
    year = {2022},
    note = {R package version 0.9.9},
    url = {https://CRAN.R-project.org/package=bsts},
  }

@Article{hyndman_forecasts_2008,
    title = {Automatic time series forecasting: the forecast package for {R}},
    author = {Rob J Hyndman and Yeasmin Khandakar},
    journal = {Journal of Statistical Software},
    volume = {26},
    number = {3},
    pages = {1--22},
    year = {2008},
    doi = {10.18637/jss.v027.i03},
  }

@book{harvey_1990,
    place={Cambridge}, 
    title={Forecasting, 
    Structural Time Series Models and the Kalman Filter},
    DOI={10.1017/CBO9781107049994},
    publisher={Cambridge University Press}, 
    author={Harvey, Andrew C.}, 
    year={1993}
}

@book{durbin_2012,
    author = {Durbin, James and Koopman, Siem Jan},
    title = "{Time Series Analysis by State Space Methods}",
    publisher = {Oxford University Press},
    year = {2012},
    month = {05},
    abstract = "{This book presents a comprehensive treatment of the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as being made up of distinct components such as trend, seasonal, regression elements and disturbance elements, each of which is modelled separately. The techniques that emerge from this approach are very flexible. Part I presents a full treatment of the construction and analysis of linear Gaussian state space models. The methods are based on the Kalman filter and are appropriate for a wide range of problems in practical time series analysis. The analysis can be carried out from both classical and Bayesian perspectives. Part I then presents illustrations to real series and exercises are provided for a selection of chapters. Part II discusses approximate and exact approaches for handling broad classes of non-Gaussian and nonlinear state space models. Approximate methods include the extended Kalman filter and the more recently developed unscented Kalman filter. The book shows that exact treatments become feasible when simulation-based methods such as importance sampling and particle filtering are adopted. Bayesian treatments based on simulation methods are also explored.}",
    isbn = {9780199641178},
    doi = {10.1093/acprof:oso/9780199641178.001.0001},
    url = {https://doi.org/10.1093/acprof:oso/9780199641178.001.0001},
}

@article{box_1976,
  title={Time series analysis: Forecasting and control San Francisco},
  author={Box, George EP and Jenkins, Gwilym M},
  journal={Calif: Holden-Day},
  year={1976}
}