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Stock Price Time Series Data Forecasting Using the Light Gradient Boosting Machine (LightGBM) Model
16 references·0 citing·50 second hop
Stock Price Time Series Data Forecasting Using the Light Gradient Boosting Machine (LightGBM) Model (2023)Stock Price Time Series Data Forecasting …Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison (2022)Profit Prediction Using ARIMA…Movement forecasting of financial time series based on adaptive LSTM-BN network (2022)Movement forecasting of finan…M5 accuracy competition: Results, findings, and conclusions (2022)M5 accuracy competition: Resu…Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models (2021)Forecasting stock price using…Forecasting Nickel futures price based on the empirical wavelet transform and gradient boosting decision trees (2021)Forecasting Nickel futures pr…Financial time series forecasting with multi-modality graph neural network (2021)Financial time series forecas…Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM) (2021)Data science approach to stoc…Stock price prediction using deep learning and frequency decomposition (2020)Stock price prediction using …On the forecasting of high‐frequency financial time series based on ARIMA model improved by deep learning (2020)On the forecasting of high‐fr…Fractional Neuro-Sequential ARFIMA-LSTM for Financial Market Forecasting (2020)Fractional Neuro-Sequential A…A Hybrid Model for Financial Time Series Forecasting—Integration of EWT, ARIMA With The Improved ABC Optimized ELM (2020)A Hybrid Model for Financial …A CNN-BiLSTM-AM method for stock price prediction (2020)A CNN-BiLSTM-AM method for st…Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices (2019)Cascading logistic regression…A fast and accurate approach for bankruptcy forecasting using squared logistics loss with GPU-based extreme gradient boosting (2019)A fast and accurate approach …Financial time series forecasting model based on CEEMDAN and LSTM (2018)Financial time series forecas…Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry (2016)Application of SARIMAX Model …Application of support vector machines in financial time series forecasting (2001)Introduction to Time Series and Forecasting (2002)Forecasting Volatility in Financial Markets: A Review (2003)A graph-based CNN-LSTM stock price prediction algorithm with leading indicators (2021)Carbon price forecasting based on CEEMDAN and LSTM (2022)Efficient Capital Markets: A Review of Theory and Empirical Work (1970)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency (1993)Research on stock trend prediction method based on optimized random forest (2021)Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques With a Novelty Feature Engineering Scheme (2021)Long memory and persistence in aggregate output (1989)Surveying stock market forecasting techniques – Part II: Soft computing methods (2008)A LSTM-based method for stock returns prediction: A case study of China stock market (2015)A survey on machine learning models for financial time series forecasting (2022)Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022 (2023)Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector (2023)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm (2023)TRNN: An efficient time-series recurrent neural network for stock price prediction (2023)Time series forecasting using a hybrid ARIMA and neural network model (2003)A CNN–LSTM model for gold price time-series forecasting (2020)An Advanced CNN-LSTM Model for Cryptocurrency Forecasting (2021)Stock market prediction using deep learning algorithms (2021)Stock Price Prediction Using CNN-BiLSTM-Attention Model (2023)Introduction to Time Series and Forecasting (2016)A deep learning framework for financial time series using stacked autoencoders and long-short term memory (2017)Predicting LQ45 financial sector indices using RNN-LSTM (2021)China’s commercial bank stock price prediction using a novel K-means-LSTM hybrid approach (2022)Predicting stock market using machine learning: best and accurate way to know future stock prices (2023)Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order (1984)The Efficient Market Hypothesis and Its Critics (2003)An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting (2022)A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis (2023)The Behavior of Stock-Market Prices (1965)Deep learning with long short-term memory networks for financial market predictions (2017)Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN) (2021)An ensemble of a boosted hybrid of deep learning models and technical analysis for forecasting stock prices (2022)Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction (2022)Data-driven stock forecasting models based on neural networks: A review (2024)Forecasting with artificial neural networks: (1998)Another look at measures of forecast accuracy (2006)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)The M5 competition: Background, organization, and implementation (2021)Forecast combinations: An over 50-year review (2022)Forecast evaluation for data scientists: common pitfalls and best practices (2022)Time-Series Forecasting (2000)A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)Hybrid wavelet-neural network models for time series (2023)A 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References

  • 2018Financial time series forecasting model based on CEEMDAN and LSTM712 cited
  • 2020A CNN-BiLSTM-AM method for stock price prediction633 cited
  • 2022M5 accuracy competition: Results, findings, and conclusions579 cited
  • 2021Financial time series forecasting with multi-modality graph neural network431 cited
  • 2020Stock price prediction using deep learning and frequency decomposition373 cited
  • 2020Fractional Neuro-Sequential ARFIMA-LSTM for Financial Market Forecasting371 cited
  • 2022Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison231 cited
  • 2021Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM)134 cited
  • 2021Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models126 cited
  • 2019Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices125 cited
  • 2022Movement forecasting of financial time series based on adaptive LSTM-BN network96 cited
  • 2021Forecasting Nickel futures price based on the empirical wavelet transform and gradient boosting decision trees94 cited
  • 2020On the forecasting of high‐frequency financial time series based on ARIMA model improved by deep learning93 cited
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Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry
92 cited
  • 2020A Hybrid Model for Financial Time Series Forecasting—Integration of EWT, ARIMA With The Improved ABC Optimized ELM86 cited
  • 2019A fast and accurate approach for bankruptcy forecasting using squared logistics loss with GPU-based extreme gradient boosting83 cited
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