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Hybrid Forecasting Methods—A Systematic Review
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Hybrid Forecasting Methods—A Systematic Review (2023)Hybrid Forecasting Methods—A Systematic R…PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews (2021)PRISMA 2020 explanation and e…Forecasting Tourist Daily Arrivals With A Hybrid Sarima–Lstm Approach (2020)Forecasting Tourist Daily Arr…PREDICTING HOUSING SALES IN TURKEY USING ARIMA, LSTM AND HYBRID MODELS (2019)PREDICTING HOUSING SALES IN T…Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States (2019)Integrating linear and nonlin…Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition (2015)Time Series Forecasting Using…Comparing Predictive Accuracy (2002)Comparing Predictive AccuracyHow well can ChatGPT forecast tourism demand? (2024)How well can ChatGPT forecast…Introduction to Grey system theory (1989)Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India (2010)Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model (2016)A hybrid model considering cointegration for interval‐valued pork price forecasting in China (2020)Effects of green energy development on population growth and employment: Evidence from shale gas exploitation in Chongqing, China (2021)Time series forecasting using a hybrid ARIMA and neural network model (2003)ARIMA forecasting of primary energy demand by fuel in Turkey (2006)Time series forecasting of petroleum production using deep LSTM recurrent networks (2018)Deep Learning Model for House Price Prediction Using Heterogeneous Data Analysis Along With Joint Self-Attention Mechanism (2021)Prediction of gold price with ARIMA and SVM (2021)Modeling and forecasting CO2 emissions in China and its regions using a novel ARIMA-LSTM model (2023)A Conceptual Model of Service Quality and Its Implications for Future Research (1985)Tourism demand modelling and forecasting—A review of recent research (2007)Deep learning with long short-term memory networks for financial market predictions (2017)A Hybrid System Based on Dynamic Selection for Time Series Forecasting (2021)Tourism demand forecasting and tourists’ search behavior: evidence from segmented Baidu search volume (2021)An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks (2023)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (2009)Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement (2009)The PRISMA 2020 statement: an updated guideline for reporting systematic reviews (2021)The PRISMA 2020 statement: an updated guideline for reporting systematic reviews (2021)The PRISMA 2020 statement: An updated guideline for reporting systematic reviews (2021)Time Series Analysis: Forecasting and Control (1977)Forecasting with artificial neural networks: (1998)A hybrid ETS–ANN model for time series forecasting (2017)Oil price forecasting using a hybrid model (2018)Hybrid structures in time series modeling and forecasting: A review (2019)Forecasting principles and practiceAnother look at measures of forecast accuracy (2006)Forecasting at Scale (2017)Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes (2012)Applications of deep learning in stock market prediction: Recent progress (2021)Machine learning in the Chinese stock market (2021)
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References

  • 2021PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews11,660 cited
  • 2002Comparing Predictive Accuracy1,745 cited
  • 2015Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition287 cited
  • 2019PREDICTING HOUSING SALES IN TURKEY USING ARIMA, LSTM AND HYBRID MODELS111 cited
  • 2020Forecasting Tourist Daily Arrivals With A Hybrid Sarima–Lstm Approach87 cited
  • 2019Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States64 cited

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  • 2024How well can ChatGPT forecast tourism demand?33 cited

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