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Variable Selection in Time Series Forecasting Using Random Forests
16 references·10 citing·34 second hop
Variable Selection in Time Series Forecasting Using Random Forests (2017)Variable Selection in Time Series Forecas…A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data (2014)A moving-average filter based…Machine Learning Strategies for Time Series Forecasting (2013)Machine Learning Strategies f…A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition (2012)A review and comparison of st…To Explain or to Predict? (2010)To Explain or to Predict?A novel hybridization of artificial neural networks and ARIMA models for time series forecasting (2010)A novel hybridization of arti…Forecasting with Exponential Smoothing (2008)Forecasting with Exponential …Forecasting and operational research: a review (2008)Forecasting and operational r…Automatic Time Series Forecasting: The forecast Package for R (2008)Automatic Time Series Forecas…Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest (2006)Forecasting Stock Index Movem…Another look at measures of forecast accuracy (2006)Another look at measures of f…25 years of time series forecasting (2006)25 years of time series forec…Combining time series models for forecasting (2003)Combining time series models …The theta model: a decomposition approach to forecasting (2000)The theta model: a decomposit…The M3-Competition: results, conclusions and implications (2000)The M3-Competition: results, …Time Series Forecasting with Neural Networks: A Comparative Study Using the Air Line Data (1998)Time Series Forecasting with …Some Recent Advances in Forecasting and Control (1968)Some Recent Advances in Forec…Feature selection with annealing for forecasting financial time series (2024)Feature selection with anneal…Modeling and Analyzing the Inventory Level for Demand Uncertainty in the VUCA World: Evidence From Biomedical Manufacturer (2022)Modeling and Analyzing the In…Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: a multisource Internet data approach (2022)Machine learning models for p…Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training (2021)Web Traffic Time Series Forec…Comparison of Machine Learning Algorithms for Predicting Crime Hotspots (2020)Comparison of Machine Learnin…Retail supply chain management: a review of theories and practices (2019)Retail supply chain managemen…Long-term forecast of energy commodities price using machine learning (2019)Long-term forecast of energy …Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans (2018)Time Series Forecasting Using…One-step ahead forecasting of geophysical processes within a purely statistical framework (2018)One-step ahead forecasting of…Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data (2018)Forecasting E-Commerce Produc…Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Forecasting with artificial neural networks: (1998)Criteria for classifying forecasting methods (2019)Daily retail demand forecasting using machine learning with emphasis on calendric special days (2020)Combining forecasts: A review and annotated bibliography (1989)Combining forecasts: A review and annotated bibliography (1990)Forecasting: Methods and Applications (1999)The tourism forecasting competition (2010)Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction (2011)Forecast combinations: An over 50-year review (2022)The Combination of Forecasts (1969)Time series forecasting using artificial neural networks methodologies: A systematic review (2018)An optimized model using LSTM network for demand forecasting (2020)Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecasting and Control (1977)Time series forecasting using a hybrid ARIMA and neural network model (2003)Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies (2017)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Testing the null hypothesis of stationarity against the alternative of a unit root (1992)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)On the use of cross-validation for time series predictor evaluation (2012)Stock price prediction using LSTM, RNN and CNN-sliding window model (2017)Time series forecasting of petroleum production using deep LSTM recurrent networks (2018)Investigating Causal Relations by Econometric Models and Cross-spectral Methods (1969)Specification Tests in Econometrics (1978)Common risk factors in the returns on stocks and bonds (1993)An updated and expanded assessment of PLS-SEM in information systems research (2017)When to use and how to report the results of PLS-SEM (2018)Predictive model assessment in PLS-SEM: guidelines for using PLSpredict (2019)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)Forecasting with temporal hierarchies (2017)Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2022)
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References

  • 2006Another look at measures of forecast accuracy5,736 cited
  • 2008Automatic Time Series Forecasting: The forecast Package for R3,538 cited
  • 2010To Explain or to Predict?2,974 cited
  • 2000The M3-Competition: results, conclusions and implications1,700 cited
  • 200625 years of time series forecasting1,589 cited
  • 2008Forecasting with Exponential Smoothing1,040 cited
  • 2010A novel hybridization of artificial neural networks and ARIMA models for time series forecasting825 cited
  • 2012A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition745 cited
  • 2013Machine Learning Strategies for Time Series Forecasting568 cited
  • 2000The theta model: a decomposition approach to forecasting507 cited
  • 2014A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data505 cited
  • 1998Time Series Forecasting with Neural Networks: A Comparative Study Using the Air Line Data419 cited
  • 1968Some Recent Advances in Forecasting and Control356 cited
  • 2008Forecasting and operational research: a review287 cited
  • 2006Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest

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244 cited
  • 2003Combining time series models for forecasting240 cited
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    • 2022Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: a multisource Internet data approach47 cited
    • 2019Retail supply chain management: a review of theories and practices41 cited
    • 2018One-step ahead forecasting of geophysical processes within a purely statistical framework36 cited
    • 2018Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans33 cited
    • 2024Feature selection with annealing for forecasting financial time series32 cited
    • 2022Modeling and Analyzing the Inventory Level for Demand Uncertainty in the VUCA World: Evidence From Biomedical Manufacturer16 cited