Prediction of InSAR deformation time-series using a long short-term memory neural network

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 paper proposes a novel method for the real-time prediction of photovoltaic (PV) power output by integrating phase space reconstruction (PSR), improved grey wolf optimization (GWO), and long short-term memory (LSTM) neural networks. The proposed method consists of three main steps. First, historical data are denoised and features are extracted using singular spectrum analysis (SSA) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Second, improved grey wolf optimization (GWO) is employed to optimize the key parameters of phase space reconstruction (PSR) and long short-term memory (LSTM) neural networks. Third, real-time predictions are made using LSTM neural networks, with dynamic updates of training data and model parameters. Experimental results demonstrate that the proposed method has significant advantages in both prediction accuracy and speed. Specifically, the proposed method achieves a mean absolute percentage error (MAPE) of 3.45%, significantly outperforming traditional machine learning models and other neural network-based approaches. Compared with seven alternative methods, our method improves prediction accuracy by 15% to 25% and computational speed by 20% to 30%. Additionally, the proposed method exhibits excellent prediction stability and adaptability, effectively handling the nonlinear and chaotic characteristics of PV power.

The use of an inertial measurement unit (IMU) to measure the motion data of the upper limb is a mature method, and the IMU has gradually become an important device for obtaining information sources to control assistive prosthetic hands. However, the control method of the assistive prosthetic hand based on the IMU often has problems with high delay. Therefore, this paper proposes a method for predicting the action intentions of upper limbs based on a long short-term memory (LSTM) neural network. First, the degree of correlation between palm movement and arm movement is compared, and the Pearson correlation coefficient is calculated. The correlation coefficients are all greater than 0.6, indicating that there is a strong correlation between palm movement and arm movement. Then, the motion state of the upper limb is divided into the acceleration state, deceleration state and rest state. The rest state of the upper limb is used as a sign to control the assistive prosthetic hand. Using the LSTM to identify the motion state of the upper limb, the accuracy rate is 99%. When predicting the action intention of the upper limb based on the angular velocity of the shoulder and forearm, the LSTM is used to predict the angular velocity of the palm, and the average prediction error of palm motion is 1.5 rad/s. Finally, the feasibility of the method is verified through experiments, in the form of holding an assistive prosthetic hand to imitate a disabled person wearing a prosthesis. The assistive prosthetic hand is used to reproduce foot actions, and the average delay time of foot action was 0.65 s, which was measured by using the method based on the LSTM neural network. However, the average delay time of the manipulator control method based on threshold analysis is 1.35 s. Our experiments show that the prediction method based on the LSTM can achieve low prediction error and delay.

Artificial neural network is widely used in the financial time series, but Long short-term memory (LSTM) neural network is rarely used in the futures market in China. In this paper, the LSTM neural network is studied by using futures data. The daily trading data of four groups of futures such as silver, copper, lithium and coking coal from December 2014 to December 2018 are used as the training object to make short-term prediction of the closing price. By comparing the Back Propagation (BP) neural network, general multi-layer LSTM neural network, and using the attention mechanism optimization LSTM contrast test, the result of the experiment shows that the futures price trend forecast time sequence, attention mechanism to promote significant effect of time sequence, and LSTM combined effect, by adjusting the parameters setting, using the improved LSTM neural network for time series prediction accuracy is higher, better generalization ability.

When forecasting ship movements, the random errors of the inertial navigation system (INS) seriously affect the accuracy of general prediction methods. In actual measurement, the main causes of the random errors are electrostatic bias and micro-electric disturbance. In response to this problem, a novel type of dual-pass Long Short-Term Memory (LSTM) neural network architecture is developed, on the basis of regular LSTM neural network. In the designed dual-pass LSTM neural network, the random drift and the noise residual of the INS are regarded as a autoregressive moving average (ARMA) and generalized autoregressive conditional heteroskedasticity (GARCH) model. Through dual-pass layers, the prediction of drift and the correction of residual errors are realized respectively in the same time. The simulation of ship heave motion was carried out on the ship motion simulation platform, and the real-time datas which are measured by the INS are inputted to the trained dual-pass LSTM netural network. The experiment proved that, when training the same source datas offline, the average Root Mean Squared Error (RMSE) percentage of conventional LSTM network was 3.94%, but when training different source datas or training online, the prediction accuracy obvious decline. In contrast, the average RMSE percentage of the dual-pass LSTM neural network was 1.05% when training offline and 1.12% when training online. Compared with conventional LSTM networks, the dual-pass LSTM network is more targeted and has better adaptability in the field of ship-motion prediction, and this network restores the motion prediction to the actual trajectory of a ship more accurately.

Air quality prediction plays an important role in preventing air pollution and improving living environment. For this prediction, many indicators can be employed to reflect the air quality, among which air quality index (AQI) is the most commonly used. However, existing methods are relatively simple and the corresponding prediction accuracy needs to be improved. Particularly, the prediction accuracy is affected by the parameter selection of methods, and the corresponding optimization problems are usually non-convex and multi-modal. Therefore, based on long short-term memory (LSTM) neural network with improved jellyfish search optimizer (IJSO), a novel hybrid model denoted by IJSO-LSTM is proposed to predict AQI for Chengdu. In order to evaluate the optimizing ability of IJSO, other variants of jellyfish search optimizer as well as other state-of-the-art meta-heuristic algorithms are applied to optimize the hyperparameters of LSTM neural network for comparison, and the results confirm that IJSO is more suitable for optimizing LSTM neural network. In addition, compared with other well-known models, the results demonstrate IJSO-LSTM has higher prediction accuracy with root-mean-square error, mean absolute error, and mean absolute percentage error controlling below 4, 3, and 4%, respectively.

To provide appropriate surgical training guidance, some skill evaluation and safety detection methods have been developed. However, these methods are difficult to provide predictive information for trainees. This paper proposes a new approach for real-time trajectory prediction of the laparoscopic instrument tip to improve surgical training and the patient safety. This paper proposes a real-time trajectory prediction model of laparoscopic instrument tip based on long short-term memory (LSTM) neural network. Meanwhile, motion state is introduced to capture more motion information of the instrument tip and improve the model performance. The feasibility, effectiveness and generalisation ability of this proposed model are preliminarily verified. The model shows satisfactory prediction accuracy for the trajectory of the laparoscopic instrument tip. LSTM neural network can accurately predict the movement trajectory of the laparoscopic instrument tip. The prediction model can play a critical role in operational risk perception in advance, which can be used in laparoscopic surgery training.

The de novo prediction of RNA tertiary structure remains a grand challenge. Predicted RNA solvent accessibility provides an opportunity to address this challenge. To the best of our knowledge, there is only one method (RNAsnap) available for RNA solvent accessibility prediction. However, its performance is unsatisfactory for protein-free RNAs. We developed RNAsol, a new algorithm to predict RNA solvent accessibility. RNAsol was built based on improved sequence profiles from the covariance models and trained with the long short-term memory (LSTM) neural networks. Independent tests on the same datasets from RNAsnap show that RNAsol achieves the mean Pearson's correlation coefficient (PCC) of 0.43/0.26 for the protein-bound/protein-free RNA molecules, which is 26.5%/136.4% higher than that of RNAsnap. When the training set is enlarged to include both types of RNAs, the PCCs increase to 0.49 and 0.46 for protein-bound and protein-free RNAs, respectively. The success of RNAsol is attributed to two aspects, including the improved sequence profiles constructed by the sequence-profile alignment and the enhanced training by the LSTM neural networks. http://yanglab.nankai.edu.cn/RNAsol/. Supplementary data are available at Bioinformatics online.

Temperature is an important load factor affecting the structural performances of bridges. The rapid acquisition of bridge temperature data is significant for bridge temperature effect analysis and assessment. On the bases of ground meteorological shared big data, a bridge temperature prediction method based on long short-term memory (LSTM) neural network is proposed. The proposed method is used to investigate the key issues of data preprocessing, model input feature selection, time-length determination, and hyper-parameter preference. Moreover, the proposed method relies on the maximum information coefficient to quantify the strongly correlated features and uses a two-layer deep LSTM neural network to improve the model’s time series information utilization and prediction capability. The constructed neural grid model is experimentally studied and verified based on the long-term measured data of the scaled bridge model in an outdoor environment. Comparative assessment with other typical time series models, such as NARX, RNN, and GRU, demonstrate that the LSTM neural network model exhibits the best generalization ability and highest temperature prediction accuracy, with a maximum absolute error of approximately 2°C and relative error below 5%. The engineering applicability and effectiveness of LSTM for bridge temperature prediction are verified.

As construction technology and project management develop, structural monitoring systems become increasingly important for ensuring large-span spatial structure safety during construction and operation. However, most of the sensors and monitoring equipment in monitoring systems are poorly serviced, resulting in frequent abnormal monitoring data, which directly leads to challenges in data analysis and structural safety assessment. In this paper, a structural response recovery method based on a long short-term memory (LSTM) neural network is proposed by studying the autocorrelation of data and the spatial correlations among data at multiple measurement points. The effectiveness and robustness of the proposed method are verified using the monitored stress data for a grid structure jacking construction process, and the influence of different data loss rates on the recovery accuracy is analysed. The recovery models are compared using a support vector machine and a Multi-Layer Perception (MLP) neural network. The proposed method can effectively restore missing data; notably, the MSE index is 0.6, and the MAPE is below 15%. The data restoration method based on the LSTM neural network is more accurate than the traditional method. Finally, the repair applicability of various types of monitored data is verified using the monitoring data from Hall F of Qingdao Jiao-dong International Airport under typhoon conditions.

To meet the demand of accurate water level prediction of the reservoir in Liuxihe River Basin, this paper proposes an improved long short-term memory (LSTM) neural network based on the Bayesian optimization algorithm and wavelet decomposition coupling. Based on the improved model, the water levels of Liuxihe Reservoir and Huanglongdai Reservoir are simulated and predicted by the 1 h prediction length, and the prediction accuracy of the improved model is verified separately by the 3, 6 and 12 h prediction lengths. The results show that: first, Bayesian optimization coupling can significantly reduce the average absolute error and root mean square error of the model and improve the overall prediction accuracy, but this algorithm is insufficient in the optimization of model extremum; Wavelet decomposition coupling can significantly reduce the outliers in model prediction and improve the accuracy of extremum, but it plays relatively weaker role in the overall optimization of the model. Second, by the prediction lengths of 1, 3, 6 and 12 h, the improved model based on the LSTM neural network and coupled with Bayesian optimization and wavelet decomposition is superior to Bayesian optimization and wavelet decomposition coupling model in overall prediction accuracy and prediction accuracy of extremum.

Oil and gas consumption for power generation has caused irreversible damage to humanity. To address the attendant effects of fossil fuel utilization, renewable energy is a good alternative. International organizations give support to countries in their transition to a green energy future. This implies that the use of renewable energy is widely supported. It is therefore recommended to utilize renewable energy as it is environmentally friendly. One such type of renewables is water energy. Water cycle has streamflow $$\left( {f_{s} } \right)$$ f s as its central component. Having reliable information about future $$f_{s}$$ f s data is essential in hydrological research, as it can help water managers to plan hydropower generation. To also ensure preparedness and mitigation of floods and drought as well as hydropower production planning and management, precise $$f_{s}$$ f s prediction is considered essential. Several modelling methods have been used lately to forecast $$f_{s}$$ f s , namely: physical methods, data-driven approaches such as shallow artificial neural networks (ANNs), and hybrid techniques. Nevertheless, they may not approximate complex relationships as accurate as deep learning techniques. In this study, an innovative deep learning technique based on long short-term memory (LSTM) neural network adapted with data preprocessing algorithm (DPA) is proposed for seasonal $$f_{s}$$ f s forecasting. Considering recent studies on $$f_{s}$$ f s forecasting, one can avow that researchers have been able to employ lag value predictors for future $$f_{s}$$ f s extrapolation, although deep learning techniques can offer good potentials for $$f_{s}$$ f s prediction with complex physical relationship. However, to the best knowledge of the authors, only very few studies have applied LSTM neural network for streamflow forecasting. In addition, there have been attempts to estimate river $$f_{s}$$ f s in Nigeria using some traditional methods though, but the effect of seasonal variation on $$f_{s}$$ f s forecasting has never been investigated in Nigeria. This is the maiden research in Nigeria that considers seasonal variation in LSTM neural network model-based $$f_{s}$$ f s forecasting. Accordingly, the novelty and key contribution of our state-of-the-art research is the development and implementation of a low-cost intelligent deep learning model based on the LSTM neural network enhanced with DPA for day-ahead $$f_{s}$$ f s forecasting. To further demonstrate the $$f_{s}$$ f s modelling capability of our technique, we have examined the performances of two different baseline approaches namely, the linear regression (LinReg) model and the adaptive linear element neural network (ADALINE-NN) model. The results of $$f_{s}$$ f s simulation indicated that the proposed LSTM neural network model has the capability to handle varnishing or exploding gradient conundrum. It is highly robust and steady with better accuracy when configured for 24-hour ahead $$f_{s}$$ f s forecasting. The LSTM neural network model outpaced both baseline approaches as model comparisons showed that it has the highest extrapolative accuracy. It presents the lowest RMSE and MAPE and the best NSME and CoC of [2.73, 1.28] m3/s, [11.16, 8.16] %, [0.91, 0.83], and [0.97, 0.95] for the rainy and dry season respectively. As the results of the LSTM neural network approach are observed to be more stable in general, it can be established that the proposed model is a practical daily $$f_{s}$$ f s forecasting technique for both the rainy and dry season.

Continuous expansion of cities has resulted in the growing traffic network to cater the demand of increasing number of vehicles. The rapid growth in population, unplanned development, and inadequate infrastructure have led to several problems like pollution and traffic congestion. Traffic congestion is one such issue with which not only metropolitan cities but also medium and small cities are dealing on day-to-day basis. Intelligent transportation system (ITS) helps in providing relief to the traffic congestion-related problems. Accurate prediction of short-term traffic flow is an important pillar of ITS. Several researchers have tried to model and predict traffic flow using different methods/techniques. Heterogeneous traffic flow makes traffic studies often more critical and challenging. This study aims to find the optimal deep bidirectional long short-term memory (LSTM) neural network to predict the short-term traffic flow under heterogeneous traffic conditions. Real data from an urban location in Delhi was collected using video cameras. Deep learning techniques like LSTM neural network and bidirectional long short-term memory (Bi-LSTM) neural network (NN) were used in this study to predict the traffic flow for the next 5 min using collected data. It was found that Bi-LSTM neural network with 2 layers gives better results as compared to LSTM NN, single–layer Bi-LSTM NN, and three-layered Bi-LSTM NN.KeywordsRoad traffic flowHeterogeneous trafficBi-LSTM neural networkDeep learning

In the experiments of measuring the strength of materials under ramp compression, accurately determining in situ particle velocity is crucial for calculating material sound speed during loading–unloading path and materials strength under high pressure. This paper proposes a machine learning approach that utilizes Long Short-Term Memory (LSTM) neural networks and Bayesian optimization algorithms to enhance the analysis of data from ramp compression strength measurement experiments. This method leverages LSTM neural networks to uncover the complex relationship between the rear interface velocity of the sample and the in situ particle velocity in numerical simulations. By using a well-trained network model, it enables direct interpretation of experimental data, leading to accurate predictions of key physical quantities along the loading and unloading paths in ramp compression experiments. A comparative analysis between theoretical curves from numerical simulations and LSTM neural network predictions shows a high degree of consistency. This approach is applied to ramp compression experiments on Ta and CuCrZr materials, demonstrating superior accuracy over the free-surface approximation and incremental impedance matching methods. Additionally, this method relies solely on the equation of state during numerical computations, eliminating the need for the complex constitutive equations required by the transfer function method, thus enhancing data processing efficiency and practicality.

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.