Forecasting economic indicators with LSTM neural networks and graph-based correlation models

Since it is one of the world's most significant financial markets, the foreign exchange (Forex) market has attracted a large number of investors. Accurately anticipating the forex trend has remained a popular but difficult issue to aid Forex traders' trading decisions. It is always a question of how precise a Forex prediction can be because of the market's tremendous complexity. The fast advancement of machine learning in recent decades has allowed artificial neural networks to be effectively adapted to several areas, including the Forex market. As a result, a slew of research articles aimed at improving the accuracy of currency forecasting has been released. The Long Short-Term Memory (LSTM) neural network, which is a special kind of artificial neural network developed exclusively for time series data analysis, is frequently used. Due to its high learning capacity, the LSTM neural network is increasingly being utilized to predict advanced Forex trading based on previous data. This model, on the other hand, can be improved by stacking it. The goal of this study is to choose a dataset using the Hurst exponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural network to forecast the trend and conduct a correlation analysis. The Hurst exponent (h) was used to determine the predictability of the Australian Dollar and United States Dollar (AUD/USD) dataset. TLS-LSTM algorithm is presented to improve the accuracy of Forex trend prediction of Australian Dollar and United States Dollar (AUD/USD). A correlation study was performed between the AUD/USD, the Euro and the Australian Dollar (EUR/AUD), and the Australian Dollar and the Japanese Yen (AUD/JPY) to see how AUD/USD movement affects EUR/AUD and AUD/JPY. The model was compared with Single-Layer Long Short-Term (SL-LSTM), Multilayer Perceptron (MLP), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Improved Firefly Algorithm Long Short-Term Memory. Based on the evaluation metrics Mean Square Error (MSE), Root Mean Square Error, and Mean Absolute Error, the suggested TLS-LSTM, whose data selection is based on the Hurst exponent (h) value of 0.6026, outperforms SL-LSTM, MLP, and CEEMDAN-IFALSTM. The correlation analysis conducted shows both positive and negative relations between AUD/USD, EUR/AUD, and AUD/JPY which means that a change in AUD/USD will affect EUR/AUD and AUD/JPY as recorded depending on the magnitude of the correlation coefficient (r).

This study explores the application of Long Short-Term Memory (LSTM) neural networks for predicting short-term price movements of the TON-IRT trading pair in the cryptocurrency market. Given the high volatility and complexity of cryptocurrency prices, traditional models like Linear Regression and ARIMA often fail to capture the underlying non-linear and temporal dependencies. To address this, we implemented an LSTM model, a type of recurrent neural network specifically designed for sequential data. The model was trained on historical hourly data, utilizing various technical indicators and lagged features to improve prediction accuracy. Our results demonstrated that the LSTM model significantly outperformed traditional methods, achieving a Mean Absolute Error (MAE) of 0.0274, a Root Mean Squared Error (RMSE) of 0.0321, and an R-squared (R²) value of 0.8743, which indicated that the model captured over 87% of the variance in the actual price data. Visual analysis of predicted versus actual prices revealed a strong alignment, though some lag in predictions during high-volatility periods was observed. The model also showed a tendency to underestimate price peaks, highlighting areas for further refinement. This study contributes to the field of blockchain trading analytics by demonstrating the effectiveness of LSTM models in addressing the unique challenges of cryptocurrency price prediction. Practical implications for traders and investors include the ability to enhance trading strategies, optimize entry and exit points, and improve risk management. Future research could integrate additional external factors, such as market sentiment and news events, or explore advanced architectures like Transformer models. By doing so, the predictive capabilities of LSTM models in volatile markets like cryptocurrency could be further refined, leading to more robust and accurate forecasting tools for financial decision-making.

Time series prediction based on LSTM neural network for top tension response of umbilical cables

Prediction of the NOx emissions from thermal power plant using long-short term memory neural network

Forecasting the short-term metro ridership is an important issue for operation management of metro systems. However, it cannot be solved well by the single long short-term memory (LSTM) neural network alone for the irregular fluctuation caused by various factors. This paper proposes a hybrid algorithm (STL-LSTM) which combines the addition mode of Seasonal-Trend decomposition based on Loess (STL) and the LSTM neural network to mitigate the influences of irregular fluctuation and improve the performance of short-term metro ridership prediction. First, the original series is decomposed into three sub-series by the addition mode of STL. Then, the LSTM neural network is employed to predict each decomposed series. Finally, all the predicted outputs are merged as the overall output. The results show that the STL-LSTM model can achieve higher accuracy than the single LSTM model, support vector regression (SVR), and the EMD-LSTM model which combines the empirical mode decomposition and the LSTM neural network.

This study constructs a model suitable for studying the performance of a Japanese speech recognition translation system based on a long short-term memory (LSTM) neural network. The impact of the matrix on the accuracy and recognition ratio of the speech recognition system is considered. This study provides a general introduction to the LSTM neural network, gives an overview of a Japanese speech recognition translation system, and presents experimental data to evaluate its performance. Then, the LSTM neural network is recommended for studying the Japanese speech translation recognition system, and a suitable model is established to conduct experiments on the system. By comparing example analyses, the experimental results show that the optimized PLSTM (Part-Aware Long Short-Term Memory) achieved an 8.09% improvement over an unoptimized LSTM network while maintaining recognition accuracy.

In response to the need for an exoskeleton to quickly identify the wearer’s movement mode in the mixed control mode, this paper studies the impact of different feature parameters of the surface electromyography (sEMG) signal on the accuracy of human motion pattern recognition using multilayer perceptrons and long short-term memory (LSTM) neural networks. The sEMG signals are extracted from the seven common human motion patterns in daily life, and the time domain and frequency domain features are extracted to build a feature parameter dataset for training the classifier. Recognition of human lower extremity movement patterns based on multilayer perceptrons and the LSTM neural network were carried out, and the final recognition accuracy rates of different feature parameters and different classifier model parameters were compared in the process of establishing the dataset. The experimental results show that the best accuracy rate of human motion pattern recognition using multilayer perceptrons is 95.53%, and the best accuracy rate of human motion pattern recognition using the LSTM neural network is 96.57%.

Experiments have proven that traditional prediction research methods have limitations in practice. Proposing countermeasures for environmental changes is the key to optimal control of the cold chain environment and reducing the lag of control effects. In this paper, a cold chain transportation environment prediction method, combining k-means++ and a long short-term memory (LSTM) neural network, is proposed according to the characteristics of the cold chain transportation environment of agricultural products. The proposed prediction model can predict the trend of cold chain environment changes in the next ten minutes, which allows cold chain vehicle managers to issue control instructions to the environmental control equipment in advance. The fusion process for temperature and humidity data measured by multiple data sensors is performed with the k-means++ algorithm, and then the fused data are fed into an LSTM neural network for prediction based on time series. The prediction error of the prediction model proposed in this paper is very satisfactory, with a root-mean-square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE) and R-squared of 0.5707, 0.2484, 0.3258, 0.0312 and 0.9660, respectively, for temperature prediction, and with an RMSE, MAE, MSE, mean absolute percentage error and R-squared of 1.6015, 1.1770, 2.5648, 0.2736 and 0.9702, respectively, for humidity prediction. Finally, the LSTM neural network and back propagation (BP) neural network are compared in order to enhance the reliability of the results. In terms of the prediction effect of the temperature and humidity in cold chain vehicles transporting agricultural products, the proposed model has a higher prediction accuracy than that of existing models and can provide strategic support for the fine management and regulation of the cold chain transportation environment.

In this paper, we propose a novel hybrid method for fault diagnosis of Proton Exchange Membrane Fuel Cells (PEMFCs) based on the combination of a physics-based model and a long short-term memory (LSTM) neural network. By incorporating the physics-based model in the fault diagnosis algorithm, we can access to several process variables not directly measured through sensors but related to the state of the PEMFC stack. The model estimates are subsequently combined with signals measured on the PEMFC stack and inputted to a LSTM neural network. The performance of the physics-guided LSTM is evaluated on an extensive dataset comprising a thousand hours of operation of a PEMFC stack under dynamic load profiles, proving the enhanced capability of the proposed fault diagnosis method in capturing complex fault patterns. Furthermore, the effectiveness of the proposed method in dealing with PEMFC aging is tested by using data from the initial phase of stack operation for algorithm training and reserving the data from the aged stack operation for the testing phase. The experimental results reveal that the proposed physics-guided LSTM method allows for a significant amelioration over purely data-driven LSTM.

The paper is focused on the application of Long Short-Term Memory (LSTM) neural network enabling patient health status prediction focusing the attention on diabetes. The proposed topic is an upgrade of a Multi-Layer Perceptron (MLP) algorithm that can be fully embedded into an Enterprise Resource Planning (ERP) platform. The LSTM approach is applied for multi-attribute data processing and it is integrated into an information system based on patient management. To validate the proposed model, we have adopted a typical dataset used in the literature for data mining model testing. The study is focused on the procedure to follow for a correct LSTM data analysis by using artificial records (LSTM-AR-), improving the training dataset stability and test accuracy if compared with traditional MLP and LSTM approaches. The increase of the artificial data is important for all cases where only a few data of the training dataset are available, as for more practical cases. The paper represents a practical application about the LSTM approach into the decision support systems (DSSs) suitable for homecare assistance and for de-hospitalization processes. The paper goal is mainly to provide guidelines for the application of LSTM neural network in type I and II diabetes prediction adopting automatic procedures. A percentage improvement of test set accuracy of 6.5% has been observed by applying the LSTM-AR- approach, comparing results with up-to-date MLP works. The LSTM-AR- neural network can be applied as an alternative approach for all homecare platforms where not enough training sequential dataset is available.

(1) Background: This study is aimed at the development of a precise and inexpensive device for flow information measurement for external flow. This novel flowmeter uses an LSTM (long short-term memory) neural network algorithm to analyze the vibration responses of the gauge plate. (2) Methods: A signal processing method using an LSTM neural network is proposed for the development of mass flow rate estimation by sensing the vibration responses of a gauge plate. An FFT (fast Fourier transform) and an STFT (short-time Fourier transform) were used to analyze the vibration characteristics of the gauge plate depending on the mass flow rate. For precise measurements, the vibration level and roughness were computed and used as input features. The actual mass flow rate measured by using a weight transducer was employed as the output features for the LSTM prediction model. (3) Results: The estimated flow rate matched the actual measured mass flow rate very closely. The deviations in measurements for the total mass flow were less than 6%. (4) Conclusions: The estimation of the mass flow rate for external flow through the proposed flowmeter by use of vibration responses analyzed by the LSTM neural network was proposed and verified.

In active heave compensation, in order to realize the smooth control of the heave compensation platform, it is necessary to use the ship motion measurement system to accurately obtain the ship displacement signal, invert the ship displacement signal, and then control the expansion and contraction of the electric cylinder so that the compensation platform remains horizontal. The ship displacement measurement system generally adopts the second integral of the acceleration sensor to obtain the ship displacement signal. During the acquisition process of the ship displacement signal, the quadratic integration process of the acceleration, and the communication process of the output control command, there is a processing lag which makes the error accumulate, resulting in a delay in the measurement of the ship motion. In order to collect the ship motion more accurately and control the heave compensation platform more precisely, this paper proposes a ship motion prediction method based on a variable step-variable sampling frequency characteristic LSTM (Long Short-Term Memory) neural network. First, we use the autocorrelation function algorithm to calculate the inherent delay of the lag in the process of signal acquisition by the measurement system. Secondly, the LSTM neural network is used to predict the inherent delay step of the lagging ship displacement signal. During the prediction process, it is found that the difference in the sampling frequency of the displacement signal will lead to a change in the step of the inherent delay—experiment in the laboratory to verify. By controlling the motion platform to simulate the motion of the ship and using the ship motion measurement system and the laser sensor system to measure the displacement signal of the motion platform synchronously, it is verified that the ship motion measurement system does have an inherent delay. Thirdly, on a sailing ship, ship displacement signals are collected by setting multiple sets of ship motion measurement systems. Finally, multiple sets of sampling frequency and multiple steps are set, and the ship motion is predicted based on the variable step-variable sampling frequency LSTM neural network. It is verified that the prediction accuracy is related to the sampling frequency of the signal collector and the prediction step of the LSTM neural network, which improves the prediction accuracy of the model and the timeliness of ship motion acquisition.

The emission of NOx in thermal power plants would cause serious pollution to the environment, and effective prediction of NOx has important significance. With the development of deep learning, many methods can effectively solve the prediction problem. In this paper, a long short-term memory (LSTM) neural network based on the generalized correntropy (GC) loss function, which is called the GC-LSTM neural network here, is proposed to predict the NOx emission. In order to make the characteristics of high-dimensional data have the same measurement scale, normalization is used to preprocess data firstly. Then, the processed historical data is used as training data to train the GC-LSTM neural network. After that, the emission of the NOx at each time instant can be predicted according to the online measured relevant data based on the established GC-LSTM neural network. Finally, prediction results under the LSTM neural network with GC, mean square error (MSE) and the mean absolute error (MAE) loss functions are given. And according to the simulation results, it can be seen that the loss function curve of the LSTM network based on GC is more stable and has better convergence. It can be seen that the proposed GC-LSTM prediction is more accurate, by comparing the probability density function (PDF) and root mean square error (RMSE) of the error between the actual values and the prediction values.

In this paper, two algorithms are proposed for short-term PV power forecasting using a long short-term memory (LSTM) neural network (NN). The first algorithm is designed to predict a single step ahead PV power, whereas the latter is capable of forecasting time horizons with variable starting points, which makes it very useful for rolling horizon based energy management algorithms. The effect of the input sequence length on the performance of the single-step model is investigated. The prediction accuracy of the multi-step model is examined with different lengths of rolling prediction horizons. It is shown that in the case of intraday rolling horizons, adding certain new predictors can effectively improve the machine performance. Hourly and half hourly data from different seasons are used to train and test the performance of the forecasting machine. Moreover, to demonstrate the superiority of the proposed LSTM based algorithms, the performance of other neural networks, namely the generalized recurrent neural network (GRNN) and the nonlinear autoregressive exogenous (NARX) neural network, is also explored.

In this paper, we develop a new Hybrid method based on machine learning algorithms for jump detection in financial time series. Jump is an important behavior in financial time series, since it implies a change in volatility. Ones can buy the volatility instrument if ones expect the volatility will bloom up in the future. A jump detection model attempts to detect short-term market instability, since it could be jumping up or down, instead of a directional prediction. The directional prediction can be considered as a momentum or trend following, which is not the focus of this paper. A jump detection model is commonly applied in a systematic fast-moving strategy, which reallocates the assets automatically. Also, a systematic opening position protection strategy can be driven by a jump detection model. For example, for a tail risk protection strategy, a pair of long call and put option order could be placed in the same time, in order to protect the open position given a huge change in volatility. One of the key differentiations of the proposed model with the classical methods of time-series anomaly detection is that, jump threshold parameters are not required to be predefined in our proposed model. Also the model is a combination of a Long short-term memory (LSTM) neural network model and a machine learning pattern recognition model. The LSTM model is applied for time series prediction, which predicts the next data point. The historical prediction errors sequence can be used as the information source or input of the jump detection model/module. The machine learning pattern recognition model is applied for jump detection. The combined model attempts to determine whether the current data point is a jump or not. LSTM neural network is a type of Recurrent Neural Networks (RNNs). LSTM records not only the recent market, but also the historical status. A stacked RNN is trained on a dataset which is mixed with normal and anomalous data. We compare the performance of the proposed Hybrid jump detection model and different pattern classification algorithms, such as k-nearest neighbors algorithm identifier, Hampel identifier, and Lee Mykland test. The model is trained and tested using real financial market data, including 11 global stock market in both developed and emerging markets in US, China, Hong Kong, Taiwan, Japan, UK, German, and Israel. The experiment result shows that the proposed Hybrid jump detection model is effective to detect jumps in terms of accuracy, comparing to the other classical jump detection methods.