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A comparison of the optimized LSTM, XGBOOST and ARIMA in Time Series forecasting
16 references·1 citing·37 second hop
A comparison of the optimized LSTM, XGBOOST and ARIMA in Time Series forecasting (2021)A comparison of the optimized LSTM, XGBOO…Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis (2020)Predicting Stock Market Trend…Stock closing price prediction based on sentiment analysis and LSTM (2019)Stock closing price predictio…An intelligent hybridization of ARIMA with machine learning models for time series forecasting (2019)An intelligent hybridization …Time series forecasting of petroleum production using deep LSTM recurrent networks (2018)Time series forecasting of pe…Evaluation of bidirectional LSTM for short-and long-term stock market prediction (2018)Evaluation of bidirectional L…A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)A Comparison of ARIMA and LST…Survey of stock market prediction using machine learning approach (2017)Survey of stock market predic…Gold Price Forecasting Using ARIMA Model (2015)Gold Price Forecasting Using …Evaluating multiple classifiers for stock price direction prediction (2015)Evaluating multiple classifie…Stock Price Prediction Using the ARIMA Model (2014)Stock Price Prediction Using …Recurrent neural network and a hybrid model for prediction of stock returns (2014)Recurrent neural network and …A hybrid ARIMA and support vector machines model in stock price forecasting (2004)A hybrid ARIMA and support ve…Fractionally integrated generalized autoregressive conditional heteroskedasticity (1996)Fractionally integrated gener…Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Autoregressive Conditional He…Time Series Analysis: Forecasting and Control. (1978)Time Series Analysis: Forecas…Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecas…Investigating Deep Stock Market Forecasting with Sentiment Analysis (2023)Investigating Deep Stock Mark…Multivariate GARCH models: a surveyGeneralized autoregressive conditional heteroskedasticity (1986)Conditional Heteroskedasticity in Asset Returns: A New Approach (1991)The distribution of realized stock return volatility (2001)Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (2007)Time Series Analysis Forecasting and Control. (1971)Time series forecasting using a hybrid ARIMA and neural network model (2003)Surveying stock market forecasting techniques – Part II: Soft computing methods (2008)A novel hybridization of artificial neural networks and ARIMA models for time series forecasting (2010)Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange (2010)Time Series Analysis, Forecasting and Control. (1971)Forecasting daily stock market return using dimensionality reduction (2016)Stock Price Prediction via Discovering Multi-Frequency Trading Patterns (2017)Forecasting with artificial neural networks: (1998)Another look at measures of forecast accuracy (2006)An optimized model using LSTM network for demand forecasting (2020)Probabilistic forecasting with temporal convolutional neural network (2020)Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization (2021)Forecasting principles and practiceDeep learning with long short-term memory networks for financial market predictions (2017)Stock price prediction using deep learning and frequency decomposition (2020)Applications of deep learning in stock market prediction: Recent progress (2021)A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks (2023)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)A desicion-theoretic generalization of on-line learning and an application to boosting (1995)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Computational Intelligence and Financial Markets: A Survey and Future Directions (2016)Literature review: Machine learning techniques applied to financial market prediction (2019)A systematic review of fundamental and technical analysis of stock market predictions (2019)Stock Market Analysis: A Review and Taxonomy of Prediction Techniques (2019)A CNN-LSTM-Based Model to Forecast Stock Prices (2020)Econometric Analysis of Cross Section and Panel Data (2001)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Principles of forecasting : a handbook for researchers and practitioners (2001)25 years of time series forecasting (2006)The politics of attention: how government prioritizes problems (2006)
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References

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  • 1996Fractionally integrated generalized autoregressive conditional heteroskedasticity2,290 cited
  • 2018A Comparison of ARIMA and LSTM in Forecasting Time Series1,382 cited
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  • 2004A hybrid ARIMA and support vector machines model in stock price forecasting1,000 cited
  • 2018Time series forecasting of petroleum production using deep LSTM recurrent networks971 cited
  • 2015Evaluating multiple classifiers for stock price direction prediction597 cited
  • 2014Recurrent neural network and a hybrid model for prediction of stock returns506 cited
  • 2020Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis467 cited
  • 2019Stock closing price prediction based on sentiment analysis and LSTM436 cited
  • 2018Evaluation of bidirectional LSTM for short-and long-term stock market prediction257 cited
  • 2017Survey of stock market prediction using machine learning approach214 cited

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  • 2019An intelligent hybridization of ARIMA with machine learning models for time series forecasting190 cited
  • 2015Gold Price Forecasting Using ARIMA Model133 cited
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    • 2023Investigating Deep Stock Market Forecasting with Sentiment Analysis45 cited