Forecasting Automobile Sales in Turkey with Artificial Neural Networks

2022 ◽  
pp. 1478-1489
Author(s):  
Aycan Kaya ◽  
Gizem Kaya ◽  
Ferhan Çebi
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Industrial Production ◽  
Economic Situation ◽  
Employment Rate ◽  
Decomposition Techniques ◽  
Dollar Exchange Rate ◽  
Financial Status ◽  
Automobile Sales ◽  
Confidence Index

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.

Forecasting Automobile Sales in Turkey with Artificial Neural Networks

2019 ◽  
Vol 6 (4) ◽  
pp. 50-60
Author(s):  
Aycan Kaya ◽  
Gizem Kaya ◽  
Ferhan Çebi
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Industrial Production ◽  
Economic Situation ◽  
Employment Rate ◽  
Decomposition Techniques ◽  
Dollar Exchange Rate ◽  
Financial Status ◽  
Automobile Sales ◽  
Confidence Index

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.

ASSESSMENT persistent time series hryvnia to the dollar US

2019 ◽  
Author(s):  
Serhii Ternov ◽  
Vasyl Fortuna
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Efficient Market Hypothesis ◽  
Long Term Memory ◽  
Characteristic Time Scale ◽  
Term Memory ◽  
Dollar Exchange Rate ◽  
Study Process ◽  
Daily Changes

Contemporary literature suggests that the effective market hypothesis is not substantiated. Instead, it suggests the Fractal Market Hypothesis (FMH). Fractal markets are characterized by long-term memory. The main feature of the fractal market is that the frequency distribution of the indicator looks the same across diffe­ rent investment horizons. In such cases, it is said that for an appropriate indicator, the phenomenon of scale invariance is observed. All daily changes are correlated with all future daily changes, all weekly changes are correlated with all future weekly changes. There is no characteristic time scale, a key characteristic of the time series. The presence of memory in the time series can be characterized by the Hearst indicator. This paper analyzes the hryvnia to US dollar exchange rate for the period 04.06.14-04.01.15. Finding the Hearst index made it possible to conclude that there is or is not long-term memory in this series. The presence of long-term memory indi­ cates that the efficient market hypothesis is unjustified. The hypothesis was tested that the longer the averaging intervals are taken into account in the model, the Hearst's index decreases. The analysis does not have great predictive power, however, it allows to identify the presence or absence of long-term memory in the study process and thus to accept or reject the hypothesis of an effective market. That is, the series under study is persistent, thus demonstrating long-term me­ mory availability. Thus, since persistence is revealed, the hypothesis of an effective market for the exchange rate yield is not confirmed, but instead can be argued for the fractality of the hryvnia / dollar exchange rate yield. Therefore, the application of the proposed approach made it possible to find the Hearst rate for the hryvnia / dollar exchange rate. The value found indicates that the effective market hypothesis is not substantiated for at least such an exchange rate.

FORECASTING MEXICAN PESO - US DOLLAR EXCHANGE RATE AFTER THE 2020 PANDEMIC WITH GARCH AND XGBOOST TIME SERIES MODELS

2020 ◽  
Vol 65 (1) ◽  
pp. 89-106
Author(s):  
Alejandro Ruiz-Olivares ◽  
Martha Elva Ramírez-Guzmán ◽  
Sandy Yaredd Trujano-Ramos
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Time Series Models ◽  
Dollar Exchange Rate ◽  
Us Dollar ◽  
Mexican Peso

scholarly journals An Application of Time Series ARIMA Forecasting Model for Predicting the Ringgit Malaysia-Dollar Exchange Rate

2018 ◽  
Vol 1 (1) ◽  
pp. 42-48
Author(s):  
Hanisah Hanun Muhamad Hatta ◽  
Faezzah Mohd Daud ◽  
Norsyafiqah Mohamad
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Moving Average ◽  
Arima Model ◽  
Secondary Data ◽  
Current Data ◽  
Forecasting Model ◽  
Dollar Exchange Rate ◽  
Future Performance ◽  
Auto Regression

ABSTRAK. Model ARIMA yang dilambangkan sebagai ARIMA (p, d, q), pada dasarnya dari Auto Regression Moving Average (ARMA) dengan proses differencing. Objek utama untuk melakukan proses ARIMA adalah memprediksi kinerja masa depan data tertentu, dengan melakukan differencing terhadap data yang jelas atau saat ini. Prediksi dihitung untuk memiliki data yang lebih baik untuk time series berikutnya. Agar memiliki data yang baik dan sempurna, ubah data non-stasioner menjadi data stasioner. Adalah mungkin untuk memiliki lebih dari satu kali proses pembedaan untuk menciptakan model ARIMA terbaik. Tulisan ini untuk menunjukkan salah satu aplikasi time series ARIMA melalui nilai tukar ringgit Malaysia terhadap dollar. Data sebelumnya yang diambil dari data sekunder adalah dari Januari 2015 hingga Desember 2017 dengan data yang disediakan setiap minggu, yang merupakan data yang dikumpulkan setiap hari Jumat. Jadi jumlah data atau observasi selama tiga tahun adalah 161. Oleh karena itu, kita bisa melakukan prediksi berdasarkan data tersebut. ABSTRACT. Time series Auto regression Integrated Moving Average (ARIMA) model, that denoted as ARIMA (p, d, q), is basically from Auto regression Moving Average (ARMA) with differencing process. The main object to do ARIMA process is to predict the future performance of certain data, by doing the differencing towards the obvious or current data. The prediction is calculated to have the better data for the next time series. In order to have a good and perfect data, transform the non-stationary data to stationary one. It is possible to have more than one time differencing process to create the best ARIMA model. This writing is to show one of the applications of time series ARIMA through the exchange rate of ringgit Malaysia to dollar. The previous data that was taken from the secondary data is from January 2015 to December 2017 with the data provided weekly, which is the data was collected on every Friday. So the number of data or observations for three years is 161. Hence, we can do the prediction based on the data.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

Bitcoin Sentiment Index, Performance and US Dollar Exchange Rate

2020 ◽  
Author(s):  
Ishfaque Ahmed Soomro ◽  
Suresh Kumar Oad Rajput ◽  
Najma Ali
Keyword(s):  
Exchange Rate ◽  
Dollar Exchange Rate ◽  
Us Dollar
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