scholarly journals Forecasting Foreign Currency Exchange Price using Long Short-Term Memory with K-Nearest Neighbor Method

2019 ◽  
Vol 9 (2) ◽  
pp. 2858-2863
Keyword(s):  
Exchange Rate ◽  
Foreign Currency ◽  
Short Term Memory ◽  
Percentage Error ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Currency Exchange ◽  
Currency Exchange Rate

With the growing population in the world, economic stability varies day by day. In case of India all banking transaction rules and regulations are taken by Reserve bank of India (RBI) whereas for other countries it is different. Therefore numerous academicians have projected their research on forecasting the currency exchange rate for diverse countryside. Foreign currency exchange rate prediction is a very pivotal task for international market. Hence researchers have explored different methods for predicting foreign currency exchange rate. In this work, we have taken Indian rupees (INR) with two different country’s data set such as Japanese yen (JPY) andChinese Yuan (CNY)for daily, weekly and monthlyprediction beforehand. We implemented a hybrid model oflong short-term memory (LSTM) with K-nearest neighbour (KNN) which gives better opening price prediction accuracy on our dataset. The accuracy of the prediction results are measured by the help of performance standards such as mean absolute percentage error (MAPE) and root mean square error (RMSE).

scholarly journals A Dense Long Short-Term Memory Model for Enhancing the Imagery-Based Brain-Computer Interface

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaofei Zhang ◽  
Tao Wang ◽  
Qi Xiong ◽  
Yina Guo
Keyword(s):  
Short Term Memory ◽  
Recognition Algorithm ◽  
Training Process ◽  
Short Term ◽  
Data Set ◽  
Static State ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Model Training ◽  
The One

Imagery-based brain-computer interfaces (BCIs) aim to decode different neural activities into control signals by identifying and classifying various natural commands from electroencephalogram (EEG) patterns and then control corresponding equipment. However, several traditional BCI recognition algorithms have the “one person, one model” issue, where the convergence of the recognition model’s training process is complicated. In this study, a new BCI model with a Dense long short-term memory (Dense-LSTM) algorithm is proposed, which combines the event-related desynchronization (ERD) and the event-related synchronization (ERS) of the imagery-based BCI; model training and testing were conducted with its own data set. Furthermore, a new experimental platform was built to decode the neural activity of different subjects in a static state. Experimental evaluation of the proposed recognition algorithm presents an accuracy of 91.56%, which resolves the “one person one model” issue along with the difficulty of convergence in the training process.

scholarly journals An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Yueling Ma ◽  
Carsten Montzka ◽  
Bagher Bayat ◽  
Stefan Kollet
Keyword(s):  
Soil Moisture ◽  
Indirect Method ◽  
Short Term Memory ◽  
Groundwater Table ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Continental Scale ◽  
Groundwater Table Depth ◽  
Long Short Term Memory

The lack of high-quality continental-scale groundwater table depth observations necessitates developing an indirect method to produce reliable estimation for water table depth anomalies (wtda) over Europe to facilitate European groundwater management under drought conditions. Long Short-Term Memory (LSTM) networks are a deep learning technology to exploit long-short-term dependencies in the input-output relationship, which have been observed in the response of groundwater dynamics to atmospheric and land surface processes. Here, we introduced different input variables including precipitation anomalies (pra), which is the most common proxy of wtda, for the networks to arrive at improved wtda estimates at individual pixels over Europe in various experiments. All input and target data involved in this study were obtained from the simulated TSMP-G2A data set. We performed wavelet coherence analysis to gain a comprehensive understanding of the contributions of different input variable combinations to wtda estimates. Based on the different experiments, we derived an indirect method utilizing LSTM networks with pra and soil moisture anomaly (θa) as input, which achieved the optimal network performance. The regional medians of test R2 scores and RMSEs obtained by the method in the areas with wtd ≤ 3.0 m were 76–95% and 0.17–0.30, respectively, constituting a 20–66% increase in median R2 and a 0.19–0.30 decrease in median RMSEs compared to the LSTM networks only with pra as input. Our results show that introducing θa significantly improved the performance of the trained networks to predict wtda, indicating the substantial contribution of θa to explain groundwater anomalies. Also, the European wtda map reproduced by the method had good agreement with that derived from the TSMP-G2A data set with respect to drought severity, successfully detecting ~41% of strong drought events (wtda ≥ 1.5) and ~29% of extreme drought events (wtda ≥ 2) in August 2015. The study emphasizes the importance to combine soil moisture information with precipitation information in quantifying or predicting groundwater anomalies. In the future, the indirect method derived in this study can be transferred to real-time monitoring of groundwater drought at the continental scale using remotely sensed soil moisture and precipitation observations or respective information from weather prediction models.

PrivItem2Vec: A privacy-preserving algorithm for top-N recommendation

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110612
Author(s):  
Zhengqiang Ge ◽  
Xinyu Liu ◽  
Qiang Li ◽  
Yu Li ◽  
Dong Guo
Keyword(s):  
Time Series ◽  
Internet Of Things ◽  
Privacy Protection ◽  
Short Term Memory ◽  
Short Term ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Term Memory ◽  
Protection Mechanism ◽  
Long Short Term Memory

To significantly protect the user’s privacy and prevent the user’s preference disclosure from leading to malicious entrapment, we present a combination of the recommendation algorithm and the privacy protection mechanism. In this article, we present a privacy recommendation algorithm, PrivItem2Vec, and the concept of the recommended-internet of things, which is a privacy recommendation algorithm, consisting of user’s information, devices, and items. Recommended-internet of things uses bidirectional long short-term memory, based on item2vec, which improves algorithm time series and the recommended accuracy. In addition, we reconstructed the data set in conjunction with the Paillier algorithm. The data on the server are encrypted and embedded, which reduces the readability of the data and ensures the data’s security to a certain extent. Experiments show that our algorithm is superior to other works in terms of recommended accuracy and efficiency.

scholarly journals Rozliczenie kredytu mieszkaniowego z niedozwolonymi walutowymi klauzulami indeksacyjnymi

Studia BAS ◽  
2021 ◽  
Vol 2 (66) ◽  
pp. 173-193
Author(s):  
Marcin Liberadzki
Keyword(s):  
Exchange Rate ◽  
Final Section ◽  
Foreign Currency ◽  
The Other ◽  
Mortgage Loan ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Better Than

This paper deals with how to settle a foreign currency exchange rate indexed mortgage loan between a bank and a consumer if the court declares that the loan agreement has an abusive clause. At present, many consumers in Poland strive to void their contracts on the grounds that they contain an abusive indexation clause, mainly referred to the CHF/PLN exchange rate. The calculations are based on a CHF indexed 30 years mortgage with decreasing monthly installments, starting in 2008. The settlement amount is calculated for two most probable scenarios: 1) the contract is declared void; 2) the contract continues but without the abusive indexation clause. One cannot determine which scenario is definitely better than the other for any party. In the final section of the article the implications for Polish banks are presented.

scholarly journals Uprawnienia kredytobiorcy zaciągającego kredyt denominowany lub indeksowany do waluty innej niż polska w świetle ustawy antyspreadowej

Ekonomia ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 39-56
Author(s):  
Magdalena Paleczna ◽  
Edyta Rutkowska-Tomaszewska
Keyword(s):  
Exchange Rate ◽  
Foreign Exchange ◽  
Foreign Currency ◽  
Consumer Credit ◽  
Competitive Position ◽  
Credit Facility ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Credit Law ◽  
Credit Contract

Rights of the borrower committing denominated or indexed loan in a foreign currency in light of the Anti-spread ActIn 2004–2008 banks offered consumer denominated loan in a foreign currency, which was a competitive position in relation to a PLN credit facility. Banks had not informed about foreign exchange differences, therefore had caused increase in household indebtedness. Banks also had reserved that consumer has to buy currency only from the bank-lender. In 2011 the Anti-spread Act was adopted, which amended banking law and consumer credit law. Creditors were obligated to inform consumer about rules of determining the manners and dates of fixing the currency exchange rate on the basis of which in particular the amount of credit, its tranches and principal and interest instalments are calculated, and the rules of converting into the currency of credit disbursement or repayment. That information and information about the rules of opening and operating the account shall be concluded in a credit contract. Borrower can repay principal and interest instalments and prepay the full or partial amount of the loan directly in that currency.

scholarly journals HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction

2018 ◽  
Author(s):  
Dejiang Kong ◽  
Fei Wu
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Location Based Services ◽  
Location Prediction ◽  
Trajectory Data ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Time ◽  
Long Short Term Memory

The widely use of positioning technology has made mining the movements of people feasible and plenty of trajectory data have been accumulated. How to efficiently leverage these data for location prediction has become an increasingly popular research topic as it is fundamental to location-based services (LBS). The existing methods often focus either on long time (days or months) visit prediction (i.e., the recommendation of point of interest) or on real time location prediction (i.e., trajectory prediction). In this paper, we are interested in the location prediction problem in a weak real time condition and aim to predict users' movement in next minutes or hours. We propose a Spatial-Temporal Long-Short Term Memory (ST-LSTM) model which naturally combines spatial-temporal influence into LSTM to mitigate the problem of data sparsity. Further, we employ a hierarchical extension of the proposed ST-LSTM (HST-LSTM) in an encoder-decoder manner which models the contextual historic visit information in order to boost the prediction performance. The proposed HST-LSTM is evaluated on a real world trajectory data set and the experimental results demonstrate the effectiveness of the proposed model.

scholarly journals Predicting machine’s performance data using the stacked long short-term memory (LSTM) neural networks

2021 ◽  
Author(s):  
Jianrong Dai
Keyword(s):  
Short Term Memory ◽  
Performance Data ◽  
Coefficient Of Determination ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Machine Performance ◽  
Machine Failure ◽  
The Mean ◽  
Long Short Term Memory

Abstract Purpose Machine Performance Check (MPC) is a daily quality assurance (QA) tool for Varian machines. The daily QA data based on MPC tests show machine performance patterns and potentially provide warning messages for preventive actions. This study developed a neural network model that could predict the trend of data variations quantitively. Methods and materials: MPC data used were collected daily for 3 years. The stacked long short-term memory (LSTM)model was used to develop the neural work model. To compare the stacked LSTM, the autoregressive integrated moving average model (ARIMA) was developed on the same data set. Cubic interpolation was used to double the amount of data to enhance prediction accuracy. After then, the data were divided into 3 groups: 70% for training, 15% for validation, and 15% for testing. The training set and the validation set were used to train the stacked LSTM with different hyperparameters to find the optimal hyperparameter. Furthermore, a greedy coordinate descent method was employed to combinate different hyperparameter sets. The testing set was used to assess the performance of the model with the optimal hyperparameter combination. The accuracy of the model was quantified by the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2). Results A total of 867 data were collected to predict the data for the next 5 days. The mean MAE, RMSE, and \({\text{R}}^{2}\) with all MPC tests was 0.013, 0.020, and 0.853 in LSTM, while 0.021, 0.030, and 0.618 in ARIMA, respectively. The results show that the LSTM outperforms the ARIMA. Conclusions In this study, the stacked LSTM model can accurately predict the daily QA data based on MPC tests. Predicting future performance data based on MPC tests will foresee possible machine failure, allowing early machine maintenance and reducing unscheduled machine downtime.

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