Abstract
Inflation is caused by the growing gap between the amount of money actively involved and the sum of products and services available for purchase. It is an economic and monetary process that manifests itself as a constant rise in prices, a fall in the current value of money. Inflation is a subject that keeps itself constantly updated in our country and around the world. The main purpose of the central banks, which are dependent on countries in the world and continue their activities, on the economy is to ensure price stability permanently. In recent years, artificial intelligence techniques have been used more and more in order to consistently predict the value of inflation in the future and to make future studies with the forecasts obtained. The aim of this study is to estimate inflation in the Turkish economy with time series analysis by using LSTM (Long Short Term Memory) model, which is one of the artificial neural networks types, on a python computer program. With this study, the estimation made by the LSTM model showed result when compared in terms of MAPE and MSE statistical analyses. It has been observed that the irregular increase in the inflation value within the country in the recent periods directly affects the success level of the models.
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References
Şengür, M.: Türkiye’de Enflasyonun Kaynağının Belirlenmesine Yönelik Ekonometrik Bir Analiz, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, pp. 47–64. (2016)
Özel, H.A.: Ekonomik Büyümenin Teorik Temelleri. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2, 63–72 (2012)
Park, W.J., Park, J.-B.: History and application of artificial neural networks in dentistry. Eur. J. Dent 12(04), 594–601 (2019). https://doi.org/10.4103/ejd.ejd_325_18
Peker, O., Göçer, İ: Yabancı Doğrudan Yatırımların Türkiye’deki İşsizliğe Etkisi: Sınır Testi Yaklaşımı. Ege Akademik Bakış Dergisi 10, 1187–1194 (2010)
İnan, Ç.A.: Raınfall – runoff prediction based on artificial neural network, a case study in La Chartreux Sprıng (2017)
Namini, S.S., Tavakoli, N., Namini, A.S.: The performance of LSTM and BiLSTM in forecasting time series. In: 2019 IEEE International Conference on Big Data, pp. 3285–3292. (2019)
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Şen, H., Efe, Ö.F. (2024). Multiple Time Series Analysis with LSTM. In: Şen, Z., Uygun, Ö., Erden, C. (eds) Advances in Intelligent Manufacturing and Service System Informatics. IMSS 2023. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-6062-0_72
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DOI: https://doi.org/10.1007/978-981-99-6062-0_72
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