Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques

Since the breakdown of the Bretton Woods system in the early 1970s, the foreign exchange (FX) market has become an important focus of both academic and practical research. There are many reasons why FX is important, but one of most important aspects is the determination of foreign investment values. Therefore, FX serves as the backbone of international investments and global trading. Additionally, because fluctuations in FX affect the value of imported and exported goods and services, such fluctuations have an important impact on the economic competitiveness of multinational corporations and countries. Therefore, the volatility of FX rates is a major concern for scholars and practitioners. Forecasting FX volatility is a crucial financial problem that is attracting significant attention based on its diverse implications. Recently, various deep learning models based on artificial neural networks (ANNs) have been widely employed in finance and economics, particularly for forecasting volatility. The main goal of this study was to predict FX volatility effectively using ANN models. To this end, we propose a hybrid model that combines the long short-term memory (LSTM) and autoencoder models. These deep learning models are known to perform well in time-series prediction for forecasting FX volatility. Therefore, we expect that our approach will be suitable for FX volatility prediction because it combines the merits of these two models. Methodologically, we employ the Foreign Exchange Volatility Index (FXVIX) as a measure of FX volatility. In particular, the three major FXVIX indices (EUVIX, BPVIX, and JYVIX) from 2010 to 2019 are considered, and we predict future prices using the proposed hybrid model. Our hybrid model utilizes an LSTM model as an encoder and decoder inside an autoencoder network. Additionally, we investigate FXVIX indices through subperiod analysis to examine how the proposed model’s forecasting performance is influenced by data distributions and outliers. Based on the empirical results, we can conclude that the proposed hybrid method, which we call the autoencoder-LSTM model, outperforms the traditional LSTM method. Additionally, the ability to learn the magnitude of data spread and singularities determines the accuracy of predictions made using deep learning models. In summary, this study established that FX volatility can be accurately predicted using a combination of deep learning models. Our findings have important implications for practitioners. Because forecasting volatility is an essential task for financial decision-making, this study will enable traders and policymakers to hedge or invest efficiently and make policy decisions based on volatility forecasting.

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Applying PCA to Deep Learning Forecasting Models for Predicting PM2.5

Sustainability ◽

10.3390/su13073726 ◽

2021 ◽

Vol 13 (7) ◽

pp. 3726

Author(s):

Sang Won Choi ◽

Brian H. S. Kim

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Time Series Prediction ◽

Fine Particulate Matter ◽

Absolute Error ◽

Learning Models ◽

Quality Factors ◽

Data Set ◽

Performance Improvements ◽

Input Variables

Fine particulate matter (PM2.5) is one of the main air pollution problems that occur in major cities around the world. A country’s PM2.5 can be affected not only by country factors but also by the neighboring country’s air quality factors. Therefore, forecasting PM2.5 requires collecting data from outside the country as well as from within which is necessary for policies and plans. The data set of many variables with a relatively small number of observations can cause a dimensionality problem and limit the performance of the deep learning model. This study used daily data for five years in predicting PM2.5 concentrations in eight Korean cities through deep learning models. PM2.5 data of China were collected and used as input variables to solve the dimensionality problem using principal components analysis (PCA). The deep learning models used were a recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM). The performance of the models with and without PCA was compared using root-mean-square error (RMSE) and mean absolute error (MAE). As a result, the application of PCA in LSTM and BiLSTM, excluding the RNN, showed better performance: decreases of up to 16.6% and 33.3% in RMSE and MAE values. The results indicated that applying PCA in deep learning time series prediction can contribute to practical performance improvements, even with a small number of observations. It also provides a more accurate basis for the establishment of PM2.5 reduction policy in the country.

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Performance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification

Scientific Programming ◽

10.1155/2021/2420254 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Sunil Kumar Prabhakar ◽

Harikumar Rajaguru ◽

Dong-Ok Won

Keyword(s):

Deep Learning ◽

Hybrid Model ◽

Language Processing ◽

Text Classification ◽

Classification Accuracy ◽

Short Term Memory ◽

Learning Algorithm ◽

Attention Mechanism ◽

Learning Models ◽

High Classification Accuracy

Over the past few decades, text classification problems have been widely utilized in many real time applications. Leveraging the text classification methods by means of developing new applications in the field of text mining and Natural Language Processing (NLP) is very important. In order to accurately classify tasks in many applications, a deeper insight into deep learning methods is required as there is an exponential growth in the number of complex documents. The success of any deep learning algorithm depends on its capacity to understand the nonlinear relationships of the complex models within data. Thus, a huge challenge for researchers lies in the development of suitable techniques, architectures, and models for text classification. In this paper, hybrid deep learning models, with an emphasis on positioning of attention mechanism analysis, are considered and analyzed well for text classification. The first hybrid model proposed is called convolutional Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism and output (CBAO) model, and the second hybrid model is called convolutional attention mechanism with Bi-LSTM and output (CABO) model. In the first hybrid model, the attention mechanism is placed after the Bi-LSTM, and then the output Softmax layer is constructed. In the second hybrid model, the attention mechanism is placed after convolutional layer and followed by Bi-LSTM and the output Softmax layer. The proposed hybrid models are tested on three datasets, and the results show that when the proposed CBAO model is implemented for IMDB dataset, a high classification accuracy of 92.72% is obtained and when the proposed CABO model is implemented on the same dataset, a high classification accuracy of 90.51% is obtained.

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Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function

Complexity ◽

10.1155/2021/5511802 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Fang Jia ◽

Boli Yang

Keyword(s):

Deep Learning ◽

Loss Function ◽

Short Term Memory ◽

Test Sample ◽

Stock Index ◽

Financial Assets ◽

Learning Models ◽

Long Short Term Memory ◽

Forecasting Volatility ◽

Deep Learning Model

Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that their models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function.

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Forecast Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

IEEE Transactions on Smart Grid ◽

10.1109/tsg.2021.3070959 ◽

2021 ◽

pp. 1-1

Author(s):

Seyed Amir Alavi ◽

Kamyar Mehran ◽

Vahid Vahidinasab ◽

Joao P. S. Catalao

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Learning Models ◽

Short Term ◽

Consensus Control ◽

Term Memory ◽

Dc Microgrids ◽

Long Short Term Memory

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A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-2326 ◽

2021 ◽

pp. 182-189

Author(s):

S. Arokiaraj ◽

Dr. N. Viswanathan

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Short Term Memory ◽

The Other ◽

Learning Models ◽

Computational Overhead ◽

Temporal Features ◽

Human Movements ◽

Proposed Model ◽

Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

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Efficient Deep Learning Models for DGA Domain Detection

Security and Communication Networks ◽

10.1155/2021/8887881 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Juhong Namgung ◽

Siwoon Son ◽

Yang-Sae Moon

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Ensemble Model ◽

Learning Models ◽

Short Term ◽

Domain Names ◽

Additional Information ◽

Domain Sequence ◽

Long Short Term Memory ◽

And Control

In recent years, cyberattacks using command and control (C&C) servers have significantly increased. To hide their C&C servers, attackers often use a domain generation algorithm (DGA), which automatically generates domain names for the C&C servers. Accordingly, extensive research on DGA domain detection has been conducted. However, existing methods cannot accurately detect continuously generated DGA domains and can easily be evaded by an attacker. Recently, long short-term memory- (LSTM-) based deep learning models have been introduced to detect DGA domains in real time using only domain names without feature extraction or additional information. In this paper, we propose an efficient DGA domain detection method based on bidirectional LSTM (BiLSTM), which learns bidirectional information as opposed to unidirectional information learned by LSTM. We further maximize the detection performance with a convolutional neural network (CNN) + BiLSTM ensemble model using Attention mechanism, which allows the model to learn both local and global information in a domain sequence. Experimental results show that existing CNN and LSTM models achieved F1-scores of 0.9384 and 0.9597, respectively, while the proposed BiLSTM and ensemble models achieved higher F1-scores of 0.9618 and 0.9666, respectively. In addition, the ensemble model achieved the best performance for most DGA domain classes, enabling more accurate DGA domain detection than existing models.

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Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

PeerJ Computer Science ◽

10.7717/peerj-cs.795 ◽

2021 ◽

Vol 7 ◽

pp. e795

Author(s):

Pooja Vinayak Kamat ◽

Rekha Sugandhi ◽

Satish Kumar

Keyword(s):

Deep Learning ◽

Anomaly Detection ◽

Short Term Memory ◽

Rotating Machinery ◽

Remaining Useful Life ◽

Operating Conditions ◽

Learning Models ◽

Vibration Data ◽

Degradation Data ◽

Useful Life

Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy functioning of the machinery, however, RUL is ill-defined. To address this issue, this study recommends using anomaly monitoring during both RUL estimator training and operation. Essential time-domain data is extracted from the raw bearing vibration data, and deep learning models are used to detect the onset of the anomaly. This further acts as a trigger for data-driven RUL estimation. The study employs an unsupervised clustering approach for anomaly trend analysis and a semi-supervised method for anomaly detection and RUL estimation. The novel combined deep learning-based anomaly-onset aware RUL estimation framework showed enhanced results on the benchmarked PRONOSTIA bearings dataset under non-varying operating conditions. The framework consisting of Autoencoder and Long Short Term Memory variants achieved an accuracy of over 90% in anomaly detection and RUL prediction. In the future, the framework can be deployed under varying operational situations using the transfer learning approach.

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Abstract 279: Multi-task Learning Improves Model Performance in Predicting Rare Catastrophic Events in Healthcare Claims Dataset

Circulation ◽

10.1161/circ.142.suppl_4.279 ◽

2020 ◽

Vol 142 (Suppl_4) ◽

Author(s):

ChienYu Chi ◽

Yen-Pin Chen ◽

Adrian Winkler ◽

Kuan-Chun Fu ◽

Fie Xu ◽

...

Keyword(s):

Cardiac Arrest ◽

Deep Learning ◽

Short Term Memory ◽

Learning System ◽

Research Database ◽

Learning Models ◽

Catastrophic Events ◽

Single Task ◽

Task Learning ◽

Hospital Cardiac Arrest

Introduction: Predicting rare catastrophic events is challenging due to lack of targets. Here we employed a multi-task learning method and demonstrated that substantial gains in accuracy and generalizability was achieved by sharing representations between related tasks Methods: Starting from Taiwan National Health Insurance Research Database, we selected adult people (>20 year) experienced in-hospital cardiac arrest but not out-of-hospital cardiac arrest during 8 years (2003-2010), and built a dataset using de-identified claims of Emergency Department (ED) and hospitalization. Final dataset had 169,287 patients, randomly split into 3 sections, train 70%, validation 15%, and test 15%.Two outcomes, 30-day readmission and 30-day mortality are chosen. We constructed the deep learning system in two steps. We first used a taxonomy mapping system Text2Node to generate a distributed representation for each concept. We then applied a multilevel hierarchical model based on long short-term memory (LSTM) architecture. Multi-task models used gradient similarity to prioritize the desired task over auxiliary tasks. Single-task models were trained for each desired task. All models share the same architecture and are trained with the same input data Results: Each model was optimized to maximize AUROC on the validation set with the final metrics calculated on the held-out test set. We demonstrated multi-task deep learning models outperform single task deep learning models on both tasks. While readmission had roughly 30% positives and showed miniscule improvements, the mortality task saw more improvement between models. We hypothesize that this is a result of the data imbalance, mortality occurred roughly 5% positive; the auxiliary tasks help the model interpret the data and generalize better. Conclusion: Multi-task deep learning models outperform single task deep learning models in predicting 30-day readmission and mortality in in-hospital cardiac arrest patients.

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Deep Learning-Based Maximum Temperature Forecasting Assisted with Meta-Learning for Hyperparameter Optimization

Atmosphere ◽

10.3390/atmos11050487 ◽

2020 ◽

Vol 11 (5) ◽

pp. 487 ◽

Cited By ~ 3

Author(s):

Trang Thi Kieu Tran ◽

Taesam Lee ◽

Ju-Young Shin ◽

Jong-Suk Kim ◽

Mohamad Kamruzzaman

Keyword(s):

Neural Network ◽

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Human Life ◽

Time Series Prediction ◽

Vital Role ◽

Maximum Temperature ◽

Hyperparameter Optimization ◽

Meta Learning

Time series forecasting of meteorological variables such as daily temperature has recently drawn considerable attention from researchers to address the limitations of traditional forecasting models. However, a middle-range (e.g., 5–20 days) forecasting is an extremely challenging task to get reliable forecasting results from a dynamical weather model. Nevertheless, it is challenging to develop and select an accurate time-series prediction model because it involves training various distinct models to find the best among them. In addition, selecting an optimum topology for the selected models is important too. The accurate forecasting of maximum temperature plays a vital role in human life as well as many sectors such as agriculture and industry. The increase in temperature will deteriorate the highland urban heat, especially in summer, and have a significant influence on people’s health. We applied meta-learning principles to optimize the deep learning network structure for hyperparameter optimization. In particular, the genetic algorithm (GA) for meta-learning was used to select the optimum architecture for the network used. The dataset was used to train and test three different models, namely the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Our results demonstrate that the hybrid model of an LSTM network and GA outperforms other models for the long lead time forecasting. Specifically, LSTM forecasts have superiority over RNN and ANN for 15-day-ahead in summer with the root mean square error (RMSE) value of 2.719 (°C).

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