A New CNN-Based Model for Financial Time Series: TAIEX and FTSE Stocks Forecasting

Kirisci M., CAĞCAĞ YOLCU Ö.

NEURAL PROCESSING LETTERS, cilt.54, sa.4, ss.3357-3374, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 54 Sayı: 4
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s11063-022-10767-z
  • Dergi Adı: NEURAL PROCESSING LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, Information Science and Technology Abstracts, INSPEC, zbMATH, DIALNET
  • Sayfa Sayıları: ss.3357-3374
  • Anahtar Kelimeler: Convolutional neural network, Financial time series, Forecasting, Deep learning, NEURAL-NETWORKS, PREDICTION, INTERVALS, LENGTHS
  • Marmara Üniversitesi Adresli: Evet

Özet

Financial time series forecasting has been becoming one of the most attractive topics in so many aspects owing to its broad implementation areas and substantial impact. Because of this reason in particular recent decades, various kinds of computational intelligence techniques like convolutional neural networks (CNNs) have been used for financial time series forecasting. However, in experiments reported so far, the number of applications of CNNs for the forecasting of financial time series seems quite a few and also in almost all-studies time sequence effect of time series is not preserved on forecasts because of image transformation. From this point of view, in this paper, by aiming to get better forecasting results and avoiding information loss which may occur the process of image transformation, we suggest a new CNN-based forecasting model that can be applied on some time series and, can successfully extract the features of them in the forecasting process. The proposed CNN forecasting model is composed of three convolutional layers and five full connected layers, also to be able to determine the nonlinear relation between input and output Relu and Elu activation functions have also been used. The suggested framework has been applied to some of the most evaluated financial time series, which are Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Financial Time Stock Exchange for London stock market data (FTSE). The results have been evaluated on different aspects as an error criterion, a regression analyses and also a visual demonstration. It has been clearly observed that CNN structure has produced outstanding forecasts compared to some other state-of-the-art forecasting tools such as different kinds of ANN, LSTM, fuzzy-based approaches, and some traditional methods.