Integrating big data driven sentiments polarity and ABC-optimized LSTM for time series forecasting

The objective of this study is to systematically compare the predictive performance of Long Short- Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), and a Hybrid LSTM–XGBoost model for next-day Bitcoin (BTC–USD) closing-price forecasting. The research method employs a quantitative time-series modeling approach using a decade-long daily Bitcoin price dataset. A strictly chronological train–test split and a one-step-ahead forecasting scheme are applied to prevent lookahead bias and ensure experimental validity. Model performance is evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), symmetric Mean Absolute Percentage Error (sMAPE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination R2 on the original price scale. The results demonstrate that the Hybrid LSTM–XGBoost model consistently outperforms the standalone LSTM and XGBoost models across all evaluation metrics, indicating superior predictive accuracy and robustness under high market volatility. The contribution of this study lies in providing a controlled, uniform, and methodologically rigorous head-to-head comparison of deep learning, machine learning, and hybrid architectures for Bitcoin price forecasting, thereby enriching the empirical literature and offering a reliable foundation for the development of adaptive decision-support systemsin volatile cryptocurrency investment environments.

BackgroundMore than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country’s policy measures.ObjectiveWe aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries.MethodsThe COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set.ResultsA total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea.ConclusionsThe CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning–based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

This study introduces discrete wavelet transform (DWT) coupled with the long-short-term memory (LSTM) model for drought prediction. We evaluate the W-LSTM model against the traditional ARIMA and conventional LSTM methods, based on the monthly precipitation data from January 1968 to January 2018 of Badeggi District in Niger State – Nigeria; which were evaluated into standardized precipitation index (SPI) of various timescales of 3, 6, 9, and 12 respectively. To understand any of the underlying features in the data, each of the SPI series was plotted graphically. Subsequently, the SPIs undergoes decomposition using Wavelet resulting in (three Details and an Approximation) components. The next step involved determining the input data based on the number of significant lags observed in PACF of each component, thereby restructuring the data to conform to LSTM’s network architecture, thereby making the modelling and forecasting of the input series by the LSTM network. Statistical evaluation metrics which include the root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), R2 and percentage of bias (PBIAS) are employed to assess the model accuracy. The W-LSTM model demonstrated superior performance over ARIMA and LSTM in forecasting, significantly reducing RMSE by up to 83.4% and improving R² by up to 147.5%. For SPI-12, W-LSTM achieved a 56.2% RMSE reduction over ARIMA and a 74.8% reduction over LSTM, with a 46.7% increase in MaPE, a 1.1% increase in R², and recorded the best PBIAS value of 2.078, compared to the other models. Similar trends were observed across SPI-6, SPI-9, and SPI-12, confirming W-LSTM’s effectiveness in minimizing errors and enhancing predictive accuracy when compared to standalone ARIMA and LSTM models.

Wind power is one of the most promising renewable energy sources, it is clean, safe and inexhaustible. However, predicting wind signal has always been challenging because the time series data is nonlinear, non-stationary and chaotic. In this paper, we provide a novel predicting framework including a recurrent neural network (RNN) structure model with long-short term memory (LS TM) units and an effective forecasting map adapted to different prediction horizons. We compare our new approach with concurrent methods and show that our new method is more effective in predicting wind power.

With the in-depth implementation of China’s “National Strategy for Building a Strong Transportation Network,” the scale of expressway construction has continued to expand. As a result, the number of high-fill and deep-cut subgrade projects under complex geological conditions has increased significantly, leading to a surge in landslide-related issues. Consequently, accurate prediction of slope displacement is of critical importance for early warning and prevention of landslide disasters. This study proposes a hybrid prediction model, VMD-MPA-LSSVM-LSTM (VMLL), which integrates Variational Mode Decomposition (VMD), Marine Predators Algorithm (MPA), Least Squares Support Vector Machine (LSSVM), and Long Short-Term Memory (LSTM) networks. Using monitoring data from the high-fill embankment slope at Hongtuyao as the research subject, the VMLL model is employed to predict slope displacement based on small-sample data. The objective is to provide a more accurate method for early warning of slope displacement. Firstly, the original monitoring data are decomposed into trend displacement components and fluctuation displacement components using Variational Mode Decomposition (VMD). Subsequently, the trend component and the fluctuation component are predicted using Least Squares Support Vector Machine (LSSVM) and Long Short-Term Memory (LSTM) networks, respectively. Finally, the Marine Predators Algorithm (MPA) is employed to optimize the hyperparameters of the predictive models. Based on this framework, a VMLL-based slope displacement prediction model is constructed. To verify the superiority of the VMLL model, a comparative analysis was conducted against LSSVM, LSTM, and the VMD-LSSVM-LSTM models. The results demonstrate that the VMLL model achieves the highest prediction accuracy, with a Mean Absolute Percentage Error (MAPE) of 0.4022%, a Mean Absolute Error (MAE) of 0.016 mm, a Root Mean Square Error (RMSE) of 0.0206 mm, a coefficient of determination (R²) of 94.08%, and a Variance Accounted For (VAF) of 96.5%. Compared with the other three models, the VMLL model reduces the MAPE, MAE, and RMSE by 30.41–67.62%, 30.38–67.40%, and 27.32–71.00%, respectively. Meanwhile, it improves the R² and VAF by 5.95–217.51% and 0.38–11.48%, respectively. These improvements clearly demonstrate that the VMLL model outperforms the other models, indicating its significant advantage over single prediction models. Furthermore, three additional datasets were used to evaluate the model’s performance. The average values of the performance evaluation metrics for these datasets were 0.6207% (MAPE), 0.0238 mm(MAE), 0.0273 mm(RMSE), 90.33%(R²), and 96.81%(VAF), respectively. These results demonstrate the high accuracy and strong robustness of the proposed model in predicting both horizontal displacement and vertical settlement of slopes, providing a reliable methodological framework for slope stability assessment and landslide disaster early warning.

Compressive strength is an important mechanical property of high-strength concrete (HSC), but testing methods are usually uneconomical, time-consuming, and labor-intensive. To this end, in this paper, a long short-term memory (LSTM) model was proposed to predict the HSC compressive strength using 324 data sets with five input independent variables, namely water, cement, fine aggregate, coarse aggregate, and superplasticizer. The prediction results were compared with those of the conventional support vector regression (SVR) model using four metrics, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R2). The results showed that the prediction accuracy and reliability of LSTM were higher with R2 = 0.997, RMSE = 0.508, MAE = 0.08, and MAPE = 0.653 compared to the evaluation metrics R2 = 0.973, RMSE = 1.595, MAE = 0.312, MAPE = 2.469 of the SVR model. The LSTM model is recommended for the pre-estimation of HSC compressive strength under a given mix ratio before the laboratory compression test. Additionally, the Shapley additive explanations (SHAP)-based approach was performed to analyze the relative importance and contribution of the input variables to the output compressive strength.

Though the ocean is sparsely populated by buoys that feature co-located instruments to measure surface winds and waves, their data is of vital importance. However, due to either minor instrumentation failure or maintenance, intermittency can be a problem for either variable. This paper attempts to mitigate the loss of valuable data from two opposite but equivalent perspectives: the conventional reconstruction of significant wave height (SWH) from Caribbean Sea buoy-observed surface wind speeds (WSP) and the inverse modeling of WSP from SWH using the long short-term memory (LSTM) network. In either direction, LSTM is strongly able to recreate either variable from its counterpart with the lowest correlation coefficient (r2) measured at 0.95, the highest root mean square error (RMSE) is 0.26 m/s for WSP, and 0.16 m for SWH. The highest mean absolute percentage errors (MAPE) for WSP and SWH are 1.22% and 5%, respectively. Additionally, in the event of complete instrument failure or the absence of a buoy in a specific area, the Simulating WAves Nearshore (SWAN) wave model is first validated and used to simulate mean and extreme SWH before, during, and after the passage of Hurricane Matthew (2016). Synthetic SWH is then fed to LSTM in a joint SWAN—LSTM model, and the corresponding WSP is reconstructed and compared with observations. Although the reconstruction is highly accurate (r2 > 0.9, RMSE < 1.3 m/s, MAPE < 0.8%), there remains great room for improvement in minimizing error and capturing high-frequency events.

Electricity is the most substantial energy form that significantly affects the development of modern life, work efficiency, quality of life, production, and competitiveness of the society in the ever-growing global world. In this respect, forecasting accurate electricity energy consumption (EEC) is fairly essential for any country?s energy consumption planning and management regarding its growth. In this study, four time-series methods; long short-term memory (LSTM) neural network, adaptive neuro-fuzzy inference system (ANFIS) with subtractive clustering (SC), ANFIS with fuzzy cmeans (FCM), and ANFIS with grid partition (GP) were implemented for the short-term one-day ahead EEC prediction. Root mean square error (RMSE), correlation coefficient (R), mean absolute error (MAE) and mean absolute percentage error (MAPE) were considered as statistical accuracy criteria. Those forecasted results by the LSTM, ANFIS-FCM, ANFIS-SC and ANFIS-GP models were evaluated by comparing with the actual data using statistical accuracy metrics. According to the testing process, the best MAPE values were obtained to be 4.47%, 3.21%, 2.34%, and 1.91% for the ANFIS-GP, ANFIS-SC, ANFIS-FCM, and LSTM, respectively. Furthermore, the best RMSE values were found as 25.94 GWh, 41.17 GWh, 29.50 GWh, and 80.14 GWh for the LSTM, ANFIS-SC, ANFIS-FCM, and ANFIS-GP models, respectively. As a consequence, the LSTM model generally outperformed all ANFIS models. The results revealed that forecasting of short-term daily EEC time series using the LSTM approach can provide high accuracy results.

This paper proposes the application of artificial intelligence to forecast the generation capacity of wind power plants by processing data through noise reduction and filtering. It subsequently employs Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for training, testing, and evaluation. Processing the initial data will help minimize noise and reduce the data space. The study focuses on preprocessing methods and selecting the appropriate neural network between LSTM and GRU. The initial data processing will assess the similarity through the Spearman rank correlation coefficient. The data used in the paper is taken from local wind turbines. The processed data will be input into the neural network for evaluation based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Percent Error (PE), and training time. The entire network simulation and evaluation process is performed using MATLAB software. The simulation results show the feasibility and suitability of the GRU network model combined with noise filtering methods, bringing high accuracy and less training time compared to the LSTM network. Specifically, the analysis contrasting the GRU network with the optimized dataset and the LSTM with the unprocessed dataset is more effective than a difference in RMSE of 15.592 and MAPE of 611.047%. Moreover, the time difference between the GRU and LSTM networks with the same dataset has a much earlier time difference from 6 to 28 seconds.

This study proposes a novel approach for multi-step time series forecasting using a stacked long-short term memory (LSTM) sequence-to-sequence autoencoder (LSTM-SAE) to handle the volatility of edible oil prices in the Indian market. The approach was implemented on Ruchi Soya Ltd. stock price dataset and compared with other deep learning models like Gated Recurrent Unit (GRU), LSTM, and Bi-directional LSTM. The LSTM SAE outperformed other models in closing price prediction based on evaluation metrics like Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The proposed approach has significant implications for stakeholders in the edible oil and oilseeds industry, including farmers, traders, and policymakers

“Normal” events are characterized as data patterns or behaviors that align with expected operational conditions, while “anomalies” are defined as deviations from these patterns, potentially signaling faults, errors, or unexpected system behaviors. The timely and accurate detection of anomalies plays a critical role in domains such as industrial manufacturing, financial transactions, and other related domains. In the context of Industry 4.0, the proliferation of sensors has resulted in a massive influx of time series data, making the anomaly detection of such multivariate time series data a popular research area. Long Short-Term Memory (LSTM) has been extensively recognized as an effective framework for modeling and processing time series data. Previous studies have combined Bi-directional Long Short-Term Memory (Bi-LSTM) architecture with Autoencoder (AE) for multivariate time series anomaly detection. However, due to the inherent limitations of LSTM, Bi-LSTM-AE still cannot overcome these drawbacks. Our study replaces the LSTM units within the Bi-LSTM-AE architecture of existing research with Working Memory Connections for LSTM units and demonstrates that this architecture performs better in the field of multivariate time series anomaly detection compared to using standard LSTM units. The model we proposed not only outperforms the baseline models but also demonstrates greater robustness across various scenarios.

Rate of penetration (ROP) is an essential factor in drilling optimization and reducing the drilling cycle. Most of the traditional ROP prediction methods are based on building physical model and single intelligent algorithms, and the efficiency and accuracy of these prediction methods are very low. With the development of artificial intelligence, high-performance algorithms make reliable prediction possible from the data perspective. To improve ROP prediction efficiency and accuracy, this paper presents a method based on particle swarm algorithm for optimization of long short-term memory (LSTM) neural networks. In this paper, we consider the Tuha Shengbei block oilfield as an example. First, the Pearson correlation coefficient is used to measure the correlation between the characteristics and eight parameters are screened out, namely, the depth of the well, gamma, formation density, pore pressure, well diameter, drilling time, displacement, and drilling fluid density. Second, the PSO algorithm is employed to optimize the super-parameters in the construction of the LSTM model to the predict ROP. Third, we assessed model performance using the determination coefficient (R 2), root mean square error (RMSE), and mean absolute percentage error (MAPE). The evaluation results show that the optimized LSTM model achieves an R 2 of 0.978 and RMSE and MAPE are 0.287 and 12.862, respectively, hence overperforming the existing methods. The average accuracy of the optimized LSTM model is also improved by 44.2%, indicating that the prediction accuracy of the optimized model is higher. This proposed method can help to drill engineers and decision makers to better plan the drilling operation scheme and reduce the drilling cycle.

Connections play a crucial role in neural network (NN) learning because they determine how information flows in NNs. Suitable connection mechanisms may extensively enlarge the learning capability and reduce the negative effect of gradient problems. In this paper, a new delay connection is proposed for Long Short-Term Memory (LSTM) unit to develop a more sophisticated recurrent unit, called Delay Connected LSTM (DCLSTM). The proposed delay connection brings two main merits to DCLSTM with introducing no extra parameters. First, it allows the output of the DCLSTM unit to maintain LSTM, which is absent in the LSTM unit. Second, the proposed delay connection helps to bridge the error signals to previous time steps and allows it to be back-propagated across several layers without vanishing too quickly. To evaluate the performance of the proposed delay connections, the DCLSTM model with and without peephole connections was compared with four state-of-the-art recurrent model on two sequence classification tasks. DCLSTM model outperformed the other models with higher accuracy and F1[Formula: see text]score. Furthermore, the networks with multiple stacked DCLSTM layers and the standard LSTM layer were evaluated on Penn Treebank (PTB) language modeling. The DCLSTM model achieved lower perplexity (PPL)/bit-per-character (BPC) than the standard LSTM model. The experiments demonstrate that the learning of the DCLSTM models is more stable and efficient.

<strong class="journal-contentHeaderColor">Abstract.</strong> A long short-term memory (LSTM) neural network is proposed to predict hurricane-forced significant wave heights (SWHs) in the Caribbean Sea (CS) based on a dataset of 20 CS, Gulf of Mexico, and western Atlantic hurricane events collected from 10 buoys from 2010–2020. SWH nowcasting and forecasting are initiated using LSTM on 0, 3, 6, 9, and 12 h horizons. Through examining study cases Hurricanes Dorian (2019), Sandy (2012), and Igor (2010), results illustrate that the model is well suited to forecast hurricane-forced wave heights much more rapidly at a significantly cheaper computational cost compared to numerical wave models, with much less required expertise. Forecasts are highly accurate with regards to observations. For example, Hurricane Dorian nowcasts had correlation (<span class="inline-formula"><i>R</i></span>), root mean square error (RMSE), and mean absolute percentage error (MAPE) values of 0.99, 0.16 m, and 2.6 %, respectively. Similarly, on the 3, 6, 9, and 12 h forecasts, results produced <span class="inline-formula"><i>R</i></span> (RMSE; MAPE) values of 0.95 (0.51 m; 7.99 %), 0.92 (0.74 m; 10.83 %), 0.85 (1 m; 13.13 %), and 0.84 (1.24 m; 14.82 %), respectively. In general, the model can provide accurate predictions within 12 h (<span class="inline-formula"><i>R</i>≥0.8</span>) and errors can be maintained at under 1 m within 6 h of forecast lead time. However, the model also consistently overpredicted the maximum observed SWHs. From a comparison of LSTM with a third-generation wave model, Simulating Waves Nearshore (SWAN), it was determined that when using Hurricane Dorian as a case example, nowcasts were far more accurate with regards to the observations. This demonstrates that LSTM can be used to supplement, but perhaps not replace, computationally expensive numerical wave models for forecasting extreme wave heights. As such, addressing the fundamental problem of phase shifting and other errors in LSTM or other data-driven forecasting should receive greater scrutiny from Small Island Developing States. To improve models results, additional research should be geared towards improving single-point LSTM neural network training datasets by considering hurricane track and identifying the hurricane quadrant in which buoy observations are made.

Accurate and efficient prediction of oilfield productivity is very important for the formulation of development and adjustment plans. Machine learning (ML) productivity prediction model can quickly obtain the productivity of oilfield development. In this paper, an oilfield development productivity prediction model based on five ML algorithms including multivariable linear regression (LR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), back propagation (BP) neural network and long short term memory (LSTM) neural network is established. Through the evaluation of model performance indicators (include the root mean square error (RMSE), the coefficient of determination (R2), the mean absolute error (MAE) and mean absolute percentage error (MAPE)), the best performance prediction model is selected. The research results show that the prediction results of LR model are greatly affected by the data of high productivity oil wells, XGBoost model are easily affected by fitting, and BP neural network model is far less effective than other models. Through comprehensive comparison of prediction results, LightGBM model has better stability and generalization performance. The difference between the prediction results of each model is mainly caused by the characteristics of the algorithm and the size of the data sets. At the same time, LSTM can predict the future oil well production based on the oil well time series observation data. The research results of this paper have guiding significance for the selection of productivity prediction model for oilfield development based on data-driven.