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Effective machine learning model combination based on selective ensemble strategy for time series forecasting
12 references·2 citing·51 second hop
Effective machine learning model combination based on selective ensemble strategy for time series forecasting (2022)Effective machine learning model combinat…Bayesian optimization based dynamic ensemble for time series forecasting (2022)Bayesian optimization based d…Strategies for time series forecasting with generalized regression neural networks (2021)Strategies for time series fo…Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm (2021)Multi-step-ahead crude oil pr…Forecasting Nickel futures price based on the empirical wavelet transform and gradient boosting decision trees (2021)Forecasting Nickel futures pr…A Hybrid System Based on Dynamic Selection for Time Series Forecasting (2021)A Hybrid System Based on Dyna…Advantages of direct input-to-output connections in neural networks: The Elman network for stock index forecasting (2020)Advantages of direct input-to…A multi-granularity heterogeneous combination approach to crude oil price forecasting (2020)A multi-granularity heterogen…Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series (2019)Ensemble approach based on ba…A brief history of forecasting competitions (2019)A brief history of forecastin…A note on the validity of cross-validation for evaluating autoregressive time series prediction (2017)A note on the validity of cro…A deep learning ensemble approach for crude oil price forecasting (2017)A deep learning ensemble appr…Cross-validation aggregation for combining autoregressive neural network forecasts (2016)Cross-validation aggregation …A Comparison of Artificial Neural Network and Time Series Models for Timber Price Forecasting (2023)A Comparison of Artificial Ne…A Hybrid Framework Using PCA, EMD and LSTM Methods for Stock Market Price Prediction with Sentiment Analysis (2022)A Hybrid Framework Using PCA,…Testing for a Unit Root in Time Series Regression (1988)Comparing Predictive Accuracy (1994)Comparing Predictive Accuracy (1995)Stock price prediction based on deep neural networks (2019)A comprehensive evaluation of ensemble learning for stock-market prediction (2020)Price forecasting through neural networks for crude oil, heating oil, and natural gas (2024)Forecasting with Exponential Smoothing : The State Space Approach (2008)On the use of cross-validation for time series predictor evaluation (2012)Evaluating time series forecasting models: an empirical study on performance estimation methods (2020)M5 accuracy competition: Results, findings, and conclusions (2022)Forecasting principles and practiceThe M3-Competition: results, conclusions and implications (2000)Another look at measures of forecast accuracy (2006)Forecasting of customer demands for production planning by local k -nearest neighbor models (2020)Investigating the accuracy of cross-learning time series forecasting methods (2020)The M5 uncertainty competition: Results, findings and conclusions (2021)Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 (2016)Forecast combinations: An over 50-year review (2022)Forecasting wholesale prices of yellow corn through the Gaussian process regression (2024)Wholesale price forecasts of green grams using the neural network (2024)Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market (2009)Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2009)Time-Varying Effects of Oil Supply Shocks on the US Economy (2013)A novel multiscale forecasting model for crude oil price time series (2021)Deep learning framework for predictive modeling of crude oil price for sustainable management in oil markets (2022)Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm (2022)Proceedings of the International Joint Conference on Neural Networks, 2003. (2003)Forecasting stock indices with back propagation neural network (2011)A hybrid fuzzy time series model based on granular computing for stock price forecasting (2014)Price graphs: Utilizing the structural information of financial time series for stock prediction (2021)A New Stock Price Forecasting Method Using Active Deep Learning Approach (2022)A knowledge graph–GCN–community detection integrated model for large-scale stock price prediction (2023)Time Series Analysis: Forecasting and Control. 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(1971)A novel hybrid method for crude oil price forecasting (2015)A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection and advanced artificial intelligence (2022)Medium- to long-term nickel price forecasting using LSTM and GRU networks (2022)A blending ensemble learning model for crude oil price forecasting (2024)Forecasting Sales by Exponentially Weighted Moving Averages (1960)Combining forecasts: A review and annotated bibliography (1989)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)Forecasts of thermal coal prices through Gaussian process regressions (2024)Office real estate price index forecasts through Gaussian process regressions for ten major Chinese cities (2024)Predictions of steel price indices through machine learning for the regional northeast Chinese market (2024)
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References

  • 2017A note on the validity of cross-validation for evaluating autoregressive time series prediction770 cited
  • 2019Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series526 cited
  • 2017A deep learning ensemble approach for crude oil price forecasting383 cited
  • 2022Bayesian optimization based dynamic ensemble for time series forecasting215 cited
  • 2019A brief history of forecasting competitions129 cited
  • 2020Advantages of direct input-to-output connections in neural networks: The Elman network for stock index forecasting112 cited
  • 2021Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm101 cited
  • 2021Forecasting Nickel futures price based on the empirical wavelet transform and gradient boosting decision trees94 cited
  • 2021A Hybrid System Based on Dynamic Selection for Time Series Forecasting86 cited
  • 2020A multi-granularity heterogeneous combination approach to crude oil price forecasting80 cited
  • 2016Cross-validation aggregation for combining autoregressive neural network forecasts75 cited
  • 2021Strategies for time series forecasting with generalized regression neural networks72 cited

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  • 2022A Hybrid Framework Using PCA, EMD and LSTM Methods for Stock Market Price Prediction with Sentiment Analysis

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46 cited
  • 2023A Comparison of Artificial Neural Network and Time Series Models for Timber Price Forecasting37 cited