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    Prediction of Vehicle Sales using Arima Model (2020)


    Swathi Chigurlapalli, Pushpalatha Maligireddy
    JCR. 2020: 3028-3037

    Abstract

    The sales forecasting of vehicles plays an important role in worldwide automobile market and its gaining attractive due to the advancement in data science approaches. However, the number of efforts undertaken in this field of research is quite small to date. Methods based on statistical learning theory are powerful instruments to get insight into internal relationships within huge empirical datasets. Therefore, they can produce reliable and even highly accurate forecasts. However, data mining algorithms have become more and more complex over the last decades. In this work, the accuracy of the prediction has the same importance as the explicability of the model. Hence, only classical data mining methods are applied here. This project presents enhanced sales forecast methodology and model for the automobile market which delivers highly accurate predictions while maintaining the ability to explain the underlying model at the same time. The representation of the economic training data is discussed, as well as its effects on the newly registered automobiles to be predicted. The methodology mainly consists of time series analysis and classical data mining algorithms, whereas the data is composed of absolute and/or relative market-specific exogenous parameters on a yearly, quarterly, or monthly base. It can be concluded that the monthly forecasts were especially improved by this enhanced methodology using absolute, normalized exogenous parameters. The main goal of this project is to consider main approaches and case studies of using machine learning for sales forecasting. The effect of machine-learning generalization has been considered. This effect can be used to make sales predictions when there is a small amount of historical data for specific sales time series in the case when a new product or store is launched. A stacking approach for building regression ensemble of single models has been studied. The results show that using stacking techniques, we can improve the performance of predictive models for sales time series forecasting.

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    Volume & Issue

    Volume 7 Issue-6

    Keywords