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Forecasting Commercial Vehicle Production Using Quantitative Techniques

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  • Badri Toppur
  • T. C. Thomas

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  • Badri Toppur & T. C. Thomas, 2023. "Forecasting Commercial Vehicle Production Using Quantitative Techniques," Contemporary Economics, Vizja University, vol. 17(1), March.
    • Handle: RePEc:wyz:journl:id:705
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    References listed on IDEAS

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    1. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    2. Lorenzo Menculini & Andrea Marini & Massimiliano Proietti & Alberto Garinei & Alessio Bozza & Cecilia Moretti & Marcello Marconi, 2021. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices," Forecasting, MDPI, vol. 3(3), pages 1-19, September.
    3. Juan Antonio Azkunaga & Leire San-Jose & Sara Urionabarrenetxea, 2013. "The impact of financial globalization and financialization on the economy in the current crisis through banking corporate governance," Contemporary Economics, Vizja University, vol. 7(3), September.
    4. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz, 2018. "Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing," International Journal of Forecasting, Elsevier, vol. 34(4), pages 748-761.
    5. Søren Johansen, 2012. "The Analysis of Nonstationary Time Series Using Regression, Correlation and Cointegration," Contemporary Economics, Vizja University, vol. 6(2), June.
    6. Marinko Škare & Saša Stjepanović, 2016. "Measuring Business Cycles: A Review," Contemporary Economics, Vizja University, vol. 10(1), March.
    7. Apostolos Ampountolas, 2021. "Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models," Forecasting, MDPI, vol. 3(3), pages 1-16, August.
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