scholarly journals US Dollar/Turkish Lira Exchange Rate Forecasting Model Based on Deep Learning Methodologies and Time Series Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1553
Author(s):  
Harun Yasar ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Exchange Rate ◽  
Time Series Analysis ◽  
Word Embedding ◽  
Learning Models ◽  
Exchange Rate Forecasting ◽  
Us Dollar ◽  
Turkish Lira ◽  
Embedding Methods

Exchange rate forecasting has been an important topic for investors, researchers, and analysts. In this study, financial sentiment analysis (FSA) and time series analysis (TSA) are proposed to form a predicting model for US Dollar/Turkish Lira exchange rate. For this purpose, the proposed hybrid model is constructed in three stages: obtaining and modeling text data for FSA, obtaining and modeling numerical data for TSA, and blending two models like a symmetry. To our knowledge, this is the first study in the literature that uses social media platforms as a source for FSA and blends them with TSA methods. To perform FSA, word embedding methods Word2vec, GloVe, fastText, and deep learning models such as CNN, RNN, LSTM are used. To the best of our knowledge, this study is the first attempt in terms of performing the FSA by using the combinations of deep learning models with word embedding methods for both Turkish and English texts. For TSA, simple exponential smoothing, Holt–Winters, Holt’s linear, and ARIMA models are employed. Finally, with the usage of the proposed model, any user who wants to make a US Dollar/Turkish Lira exchange rate forecast will be able to make a more consistent and strong exchange rate forecast.

scholarly journals TSViz: Demystification of Deep Learning Models for Time-Series Analysis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 67027-67040 ◽  
Author(s):  
Shoaib Ahmed Siddiqui ◽  
Dominique Mercier ◽  
Mohsin Munir ◽  
Andreas Dengel ◽  
Sheraz Ahmed
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Learning Models ◽  
Series Analysis

scholarly journals An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System

2019 ◽  
Vol 9 (15) ◽  
pp. 2980 ◽  
Author(s):  
Muhammad Yasir ◽  
Mehr Yahya Durrani ◽  
Sitara Afzal ◽  
Muazzam Maqsood ◽  
Farhan Aadil ◽  
...  
Keyword(s):  
Deep Learning ◽  
Exchange Rate ◽  
Foreign Exchange ◽  
Foreign Currency ◽  
Foreign Exchange Rate ◽  
Exchange Rate Forecasting ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Us Dollar ◽  
The Exchange Rate

Financial time series analysis is an important research area that can predict various economic indicators such as the foreign currency exchange rate. In this paper, a deep-learning-based model is proposed to forecast the foreign exchange rate. Since the currency market is volatile and susceptible to ongoing social and political events, the proposed model incorporates event sentiments to accurately predict the exchange rate. Moreover, as the currency market is heavily dependent upon highly volatile factors such as gold and crude oil prices, we considered these sensitive factors for exchange rate forecasting. The validity of the model is tested over three currency exchange rates, which are Pak Rupee to US dollar (PKR/USD), British pound sterling to US dollar (GBP/USD), and Hong Kong Dollar to US dollar (HKD/USD). The study also shows the importance of incorporating investor sentiment of local and foreign macro-level events for accurate forecasting of the exchange rate. We processed approximately 5.9 million tweets to extract major events’ sentiment. The results show that this deep-learning-based model is a better predictor of foreign currency exchange rate in comparison with statistical techniques normally employed for prediction. The results present evidence that the exchange rate of all the three countries is more exposed to events happening in the US.

Deep learning models for forecasting aviation demand time series

2021 ◽  
Author(s):  
Andreas Kanavos ◽  
Fotios Kounelis ◽  
Lazaros Iliadis ◽  
Christos Makris
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Learning Models

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

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