Forecasting Automobile Sales in Turkey with Artificial Neural Networks

This study aims to reveal significant factors which affect automobile sales and estimate the automobile sales in Turkey by using Artificial Neural Network (ANN), ARIMA, and time series decomposition techniques. The forecasting model includes automobile sales, automobile price, Euro and Dollar exchange rate, employment rate, consumer confidence index, oil prices and industrial production confidence index, the probability of buying an automobile, female employment rate, general economic situation, the expectation of general economic situation, financial status of households, expectation of financial status of households. According to the regression results, changes in Dollar exchange rate, the expectation of financial status of households, seasonally adjusted industrial production index, logarithmic form of automobile sales before-one-month which have a significant effect on automobile sales, are found to be the significant variables. The results show that ANN has a better estimation performance with MAPE=1.18% and RMSE=782 values than ARIMA and time series decomposition techniques.

Demand Forecasting of The Automobile Sales Using Least Square, Single Exponential Smoothing and Double Exponential Smoothing

Sales strategies require the right managerial in marketing products with the development of technology and communication, the decision making in product sales supported by complete data and can be analyzed into intelligence business solutions. The research discussed and provided solutions about how to forecast future demand targets from a set of data history by making a predictive model of product demand in the real case. The research study was obtained from automobile sales, which the company probably set the strategy from the forecast result of automobile sales by the system in the future. The research used forecasting methods such as Least Square, Single Exponential Smoothing, and Double Exponential Smoothing to achieve a small percentage of prediction error. The dataset was collected from Mitsubishi Motors Corporation which obtained 60 samples of popular product types such as Pajero, FE and L300 from 2014 to 2018 over a period of months. The experimental results reported that Double Exponential Smoothing has given a better performance than other methods. The forecasting result of Pajero reached the MAPE of 3.26%, FE reached the MAPE of 3.24%, and L300 reached the MAPE of 3.37%. This study indicates that the selection of the forecasting method depends on the actual data pattern and the adjustment of the parameters in predicting future points.

Emissions calendar changes weaken the palladium market

Significance Supply this year has been mildly affected by pandemic disruption, and inventories have fallen due to lower levels of recycling. Demand has been tempered by weak automobile sales, although rising emissions standards should mean that more palladium is needed for every hybrid, diesel or petrol car that is produced. Impacts Low confidence and high uncertainty are weighing on car sales; the annual rate of US car sales is 25% lower in 2020 than in recent years. With metal prices high and automakers under cost pressure, thrifting and substitution in auto-catalysts are the main threats to palladium. High palladium prices make stealing catalytic converters worthwhile: 8,248 cars were stolen in London last year, a sevenfold increase.

Analysis on Fund Management Strategy of Auto Dealer Group

<p class="16" align="justify">In recent years, with the further development of the automobile industry, more and more enterprises have joined the industry, and the automobile sales competition has become increasingly fierce. Chinese automobile enterprises are facing severe internal and external challenges. The intensification of competitiveness makes capital risk increase. In order to develop stably and not be eliminated in the market, enterprises must take corresponding measures to solve the problems existing in capital management.</p>