Türkiye Demiryolu Yolcu Taşıma Talebinin Tahmini

Cakir, Fatma Betul; Tosun, Hümeyra Bolakar

doi:10.29130/dubited.769092

Cited by 3 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result of the study, it has been argued that ANN is more successful than the ARIMA model in estimating unemployment, and the unemployment rate estimated by the developed model is very close to the truth. Çakır and Bolakar Tosun (2021), in this study, estimated the number of railway passengers using the variables that are effective on railway passenger transport, with Multivariate Regression (MDR) analysis and ANN models. As a result of the study, it has been argued that the most appropriate estimation is obtained by ANN and ANN can be used as a source in demand estimations.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Forecasting of Turkey's Unemployment Rate for Future Periods With Artificial Neural Networks

Karahan

ÇETİNTAŞ

2022

Erciyes Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

View full text Add to dashboard Cite

In world economies, in order to achieve high national income level, employment has an important effect. Therefore, it is necessary for unemployment to be highly low. Labor force structure of a country specifies the state of that country, and that unemployment rates are at high levels is an indicator of that there is a deviation in the development and growth rate of country economy. In this context, forecasting unemployment rates in the next periods of Turkey, in order to develop solution suggestions for unemployment problem which is one of the most important problems of today, and make contribution to improving country economy, it was decided to conduct such a study. In this forecasting study conducted, due to the fact that the risk to obtain wrong results with traditional methods is high in the solutions of chaotic contented problems, it was decided to be used ANN (Artificial Neural Network) method, which presents healthy solutions of the chaotic problems including partly erroneous or over deviating data and is one of the contemporary methods, commonly used in the literature. In the study, utilizing the monthly basic economic indicators of Turkey belonging to the period of 2005-2018, forecast of unemployment rate for the next periods was made by ANN method, and the data belonging to totally six basic economic indicators were used in the forecast. As a conclusion of the study, it was identified that the forecast made by the model developed produced the reliable results that are quite close to the reality.

show abstract

Section: Artificial Neural Networkmentioning

confidence: 99%

Forecasting of Turkey's Unemployment Rate for Future Periods With Artificial Neural Networks

Karahan

ÇETİNTAŞ

2022

Erciyes Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

View full text Add to dashboard Cite

show abstract

Forecasting the Number of Passengers in Rail System by Deep Learning Algorithms

Asutay,

Uğurlu

2024

EAI/Springer Innovations in Communication and Computing

View full text Add to dashboard Cite

Wind speed prediction using LSTM and ARIMA time series analysis models: A case study of Gelibolu

Demirtop,

Sevli

2024

Turkish Journal of Engineering

View full text Add to dashboard Cite

Wind energy stands out as a prominent renewable energy source, characterized by its high efficiency, feasibility, and wide applicability. Nonetheless, the integration of wind energy into the electrical system encounters significant obstacles due to the unpredictability and variability of wind speed. Accurate wind speed prediction is essential for estimating the short-, medium-, and long-term power output of wind turbines. Various methodologies and models exist for wind speed time series prediction. This research paper proposes a combination of two approaches to enhance forecasting accuracy: deep learning, particularly Long Short-Term Memory (LSTM), and the Autoregressive Integrated Moving Average (ARIMA) model. LSTM, by retaining patterns over longer periods, improves prediction rates. Meanwhile, the ARIMA model enhances the likelihood of staying within predefined boundaries. The study utilizes daily average wind speed data from the Gelibolu district of Çanakkale province spanning 2014 to 2021. Evaluation using the root mean square error (RMSE) shows the superior forecast accuracy of the LSTM model compared to ARIMA. The LSTM model achieved an RMSE of 6.3% and a mean absolute error of 16.67%. These results indicate the potential utility of the proposed approach in wind speed forecasting, offering performance comparable to or exceeding other studies in the literature.

show abstract

Türkiye Demiryolu Yolcu Taşıma Talebinin Tahmini

Cited by 3 publications

References 10 publications

Forecasting of Turkey's Unemployment Rate for Future Periods With Artificial Neural Networks

Forecasting of Turkey's Unemployment Rate for Future Periods With Artificial Neural Networks

Forecasting the Number of Passengers in Rail System by Deep Learning Algorithms

Wind speed prediction using LSTM and ARIMA time series analysis models: A case study of Gelibolu

Contact Info

Product

Resources

About