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Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins
16 references·0 citing·36 second hop
Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins (2023)Comparative Analysis of Machine Learning,…Machine learning in the Chinese stock market (2021)Machine learning in the Chine…The impact of COVID-19 on emerging stock markets (2020)The impact of COVID-19 on eme…The COVID-19 Outbreak and Affected Countries Stock Markets Response (2020)The COVID-19 Outbreak and Aff…Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets (2020)Economic impact of government…Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns (2020)Death and contagious infectio…COVID-19 and the march 2020 stock market crash. Evidence from S&P1500 (2020)COVID-19 and the march 2020 s…Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis (2020)Co-movement of COVID-19 and B…Forecasting principles and practice (2018)Forecasting principles and pr…Automatic time series forecasting: the forecast package for R (2017)Automatic time series forecas…A new metric of absolute percentage error for intermittent demand forecasts (2016)A new metric of absolute perc…A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data (2014)A moving-average filter based…Automatic Time Series Forecasting: The forecast Package for R (2008)Automatic Time Series Forecas…Time series forecasting using a hybrid ARIMA and neural network model (2003)Time series forecasting using…Forecasting with artificial neural networks: (1998)Forecasting with artificial n…Distribution of the Estimators for Autoregressive Time Series With a Unit Root (1979)Distribution of the Estimator…Distribution of the Estimators for Autoregressive Time Series With a Unit Root (1979)Distribution of the Estimator…Book Review: Time Series Analysis: Forecasting and Control (1977)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)Financial time series forecasting using support vector machines (2003)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)A Monetary History of the United States 1867-1960 (1964)Time Series Analysis Forecasting and Control. (1971)Statistical analysis of cointegration vectors (1988)Testing for a Unit Root in Time Series Regression (1988)Bounds testing approaches to the analysis of level relationships (2001)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)Another look at measures of forecast accuracy (2006)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 methods and applications (1993)The M4 Competition: 100,000 time series and 61 forecasting methods (2019)Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms (2021)Forecast evaluation for data scientists: common pitfalls and best practices (2022)The impact of the SARS outbreak on Taiwanese hotel stock performance: An event-study approach (2006)Stock prices and geographic proximity of information: Evidence from the Ebola outbreak (2017)Coronavirus (COVID-19) — An epidemic or pandemic for financial markets (2020)Stock markets’ reaction to COVID-19: Cases or fatalities? (2020)Econometric Analysis of Panel Data (2021)An Empirical Evaluation of Accounting Income Numbers (1968)The Adjustment of Stock Prices to New Information (1969)Event Studies in Economics and Finance (1997)COVID-19 and stock market volatility: An industry level analysis (2020)The impact of the COVID-19 lockdown on stock market performance: evidence from Vietnam (2020)Dynamic connectedness between stock markets in the presence of the COVID-19 pandemic: does economic policy uncertainty matter? (2021)A hybrid ARIMA and support vector machines model in stock price forecasting (2004)A novel hybridization of artificial neural networks and ARIMA models for time series forecasting (2010)A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)Forecasting at Scale (2017)Testing for unit roots in heterogeneous panels (2003)
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References

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  • 2003Time series forecasting using a hybrid ARIMA and neural network model4,583 cited
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  • 2016A new metric of absolute percentage error for intermittent demand forecasts1,183 cited
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  • 2020COVID-19 and the march 2020 stock market crash. Evidence from S&P1500726 cited
  • 2020The impact of COVID-19 on emerging stock markets675 cited
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  • 2014A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

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512 cited
  • 2020Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis476 cited
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