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Cited by

• 2019Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model337 cited
• 2003A comparison of three different approaches to tourist arrival forecasting322 cited
• 2022Bayesian optimization based dynamic ensemble for time series forecasting215 cited
• 2020SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain188 cited
• 2021An Advanced CNN-LSTM Model for Cryptocurrency Forecasting183 cited
• 2022Revisiting Research on Positive Psychology in Second and Foreign Language Education: Trends and Directions164 cited
• 2023Towards Innovative Research Approaches to Investigating the Role of Emotional Variables in Promoting Language Teachers’ and Learners’ Mental Health160 cited
• 2020Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor140 cited
• 2020A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques137 cited
• 2019Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models103 cited
• 2010Post-traumatic stress disorder: a right temporal lobe syndrome?97 cited
• 2016Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods92 cited