Forecasting of Forex Time Series Data Based on Deep Learning

Ni, Lina; Li, Yujie; Wang, Xiao; Zhang, Jinquan; Yu, Jiguo; Qi, Chengming

doi:10.1016/j.procs.2019.01.189

Cited by 83 publications

(32 citation statements)

References 29 publications

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“…Semantic analysis improves the prediction rate by detecting positive and negative news headlines and predicting accordingly. Most of the other researchers have used the neural network-based models that were capable of predicting time series (e.g., Ranjit, Shruti, Sital, and Subarna [44]; Lina, Yujie, Xiao, Jinquan, Jiguo, and Chengming [45]; Jacek and Piotr [46]). The neural network was an evolutionary discovery since it predicts using hidden neurons and is also capable of adjusting the weighted values which help with providing good accuracy.…”

Section: Methodsmentioning

confidence: 99%

“…However, due to the expansion order and delay block size, the network size becomes pretty large which is a major drawback of this model. Lina, Yujie, Xiao, Jinquan, Jiguo, and Chengming [45] proposed a model that predicts the time series of FOREX using the C-RNN method. C-RNN was created using a convolutional neural network and recurrent neural network.…”

Section: Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Recent Advancements in FOREX Currency Prediction

Islam¹,

Hossain

Rahman³

et al. 2020

Algorithms

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In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on “Elsevier”, “Springer”, and “IEEE Xplore” that predicted future FOREX prices within the stipulated time. Our research shows that in recent years, researchers have been interested mostly in neural networks models, pattern-based approaches, and optimization techniques. Our review also shows that many deep learning algorithms, such as gated recurrent unit (GRU) and long short term memory (LSTM), have been fully explored and show huge potential in time series prediction.

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Section: Methodsmentioning

confidence: 99%

Section: Neural Networkmentioning

confidence: 99%

A Review on Recent Advancements in FOREX Currency Prediction

Islam¹,

Hossain

Rahman³

et al. 2020

Algorithms

View full text Add to dashboard Cite

show abstract

“…In air pollution estimation studies via machine learning, it is clearly seen that methods based on artificial neural networks stand out regardless of whether the target pollutant is PM or not. Considering the success of deep learning techniques in many other application domains [27][28][29] , it is inevitable that the studies for air pollution prediction have recently focused on deep learning methods.…”

Section: Related Workmentioning

confidence: 99%

Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting

Kaya

Öğüdücü

2020

Sci Rep

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Growing metropolitan areas bring rapid urbanization and air pollution problems. As diseases and mortality rates increase because of the air pollution problem, it becomes a necessity to estimate the air pollution density and inform the public to protect the health. Air pollution problem displays contextual characteristics such as meteorological conditions, industrial and technological developments, traffic problem etc. that change from country to country and also from city to city. In this study, we determined PM 10 as the target pollutant and designed a new deep learning based air quality forecasting model, namely DFS (Deep Flexible Sequential). Our study uses real world hourly data from Istanbul, Turkey between 2014 and 2018 to forecast the air pollution 4, 12, and 24 hours before. DFS model is a hybrid & flexible deep model including Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). The proposed model also is capable of generalization with standard and flexible Dropout layers. Through flexible Dropout layer, the model also obtains flexibility to adapt changing window sizes in sequential modelling. Moreover, this model can be applied to other air pollution time series data problems with small modifications on parameters by taking into account the nature of the data set.

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“…FOREX market is the world's largest financial market. The daily trading volume has been increased six trillion dollars which it's 45% of the transaction volume comes from terminal retail customers (Ni et al, 2019). There are several techniques in FOREX trading, one of them is forecasting FOREX.…”

Section: Introductionmentioning

confidence: 99%

Currency movement forecasting using time series analysis and long short-term memory

Putri

Halim

2020

Int. J. Ind. Optim.

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Foreign exchange is one type of investment, which its goal is to minimize losses that could occur. Forecasting is a technique to minimize losses when investing. The purpose of this study is to make foreign exchange predictions using a time series analysis called Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-term memory methods. This study uses the daily EUR / USD exchange rates from 2014 to March 2020. The data are used as the model to predict the value of the foreign exchange market in April 2020. The model obtained will be used for predictions in April 2020, where the RMSE values obtained from time series analysis (ARIMA) with a window size of 100 days and LSTM sequentially as follows 0.00527 and 0.00509. LSTM produces lower RMSE values than ARIMA. LSTM has better prediction results; this is because the LSTM has the ability to learn so that it can utilize a large amount of data while ARIMA cannot use it. ARIMA does not have the ability to learn even though given a large amount of data it gives poor forecasting results. The ARIMA prediction is the same as the values of the previous day.

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Forecasting of Forex Time Series Data Based on Deep Learning

Cited by 83 publications

References 29 publications

A Review on Recent Advancements in FOREX Currency Prediction

A Review on Recent Advancements in FOREX Currency Prediction

Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting

Currency movement forecasting using time series analysis and long short-term memory

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