Search Results (671)

Landslide disasters frequently result in significant casualties and property losses, underscoring the critical importance of research on landslide displacement prediction. This paper introduces an approach combining improved empirical mode decomposition (ICEEMDAN) and singular entropy-enhanced singular spectrum analysis (SSA) to predict landslide displacement using a time series short-duration memory network (LSTM). Initially, ICEEMDAN decomposes the landslide displacement time series into trend and periodic terms. SSA is then employed to denoise these components before fitting the trend term with LSTM. Pearson correlation analysis is utilized to identify characteristic factors within the LSTM model, followed by predictions using a multivariate LSTM model. The empirical results from the Baijiabao landslide in the Three Gorges Reservoir area demonstrate that the joint ICEEMDAN-SSA approach, when combined with LSTM modeling, outperforms the separate applications of SSA and ICEEMDAN, as well as other models such as RNN and SVM. Specifically, the ICEEMDAN-SSA-LSTM model achieves an RMSE of 6.472 mm and an MAE of 4.992 mm, which are considerably lower than those of the RNN model (19.945 mm and 15.343 mm, respectively) and the SVM model (16.584 mm and 11.748 mm, respectively). Additionally, the R² value for the ICEEMDAN-SSA-LSTM model is 97.5%, significantly higher than the RNN model’s 72.3% and the SVM model’s 92.8%. By summing the predictions of the trend and periodic terms, the cumulative displacement prediction is obtained, indicating the superior accuracy of the ICEEMDAN-SSA-LSTM model. This model provides a new benchmark for precise landslide displacement prediction and contributes valuable insights to related research. Full article

Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research. Full article

Accurately estimating the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for maintaining the safe and stable operation of rechargeable battery management systems. However, this task is often challenging due to the complex temporal dynamics. Recently, attention-based networks, such as Transformers and Informer, have been the popular architecture in time series forecasting. Despite their effectiveness, these models with abundant parameters necessitate substantial training time to unravel temporal patterns. To tackle these challenges, we propose a straightforward MLP-Mixer-based architecture named “Intra–Inter Patch Mixer” (IIP-Mixer), which leverages the strengths of multilayer perceptron (MLP) models to capture both local and global temporal patterns in time series data. Specifically, it extracts information using an MLP and performs mixing operations along both intra-patch and inter-patch dimensions for battery RUL prediction. The proposed IIP-Mixer comprises parallel dual-head mixer layers: the intra-patch mixing MLP, capturing local temporal patterns in the short-term period, and the inter-patch mixing MLP, capturing global temporal patterns in the long-term period. Notably, to address the varying importance of features in RUL prediction, we introduce a weighted loss function in the MLP-Mixer-based architecture, marking the first time such an approach has been employed. Our experiments demonstrate that IIP-Mixer achieves competitive performance in battery RUL prediction, outperforming other popular time series frameworks, such as Informer and DLinear, with relative reductions in mean absolute error (MAE) of 24% and 10%, respectively. Full article

Photovoltaic (PV) power fluctuates with weather changes, and traditional forecasting methods typically decompose the power itself to study its characteristics, ignoring the impact of multidimensional weather conditions on the power decomposition. Therefore, this paper proposes a short-term PV power generation method based on MVMD (multivariate variational mode decomposition) feature extraction and the Informer model. First, MIC correlation analysis is used to extract weather features most related to PV power. Next, to more comprehensively describe the relationship between PV power and environmental conditions, MVMD is used for time–frequency synchronous analysis of the PV power time series combined with the highest MIC correlation weather data, obtaining frequency-aligned multivariate intrinsic modes. These modes incorporate multidimensional weather factors into the data-decomposition-based forecasting method. Finally, to enhance the model’s learning capability, the Informer neural network model is employed in the prediction phase. Based on the input PV IMF time series and associated weather mode components, the Informer prediction model is constructed for training and forecasting. The predicted results of different PV IMF modes are then superimposed to obtain the total PV power generation. Experiments show that this method improves PV power generation accuracy, with an MAPE value of 4.31%, demonstrating good robustness. In terms of computational efficiency, the Informer model’s ability to handle long sequences with sparse attention mechanisms reduces training and prediction times by approximately 15%, making it faster than conventional deep learning models. Full article

In multivariate and multistep time series prediction research, we often face the problems of insufficient spatial feature extraction and insufficient time-dependent mining of historical series data, which also brings great challenges to multivariate time series analysis and prediction. Inspired by the attention mechanism and residual module, this study proposes a multivariate time series prediction method based on a convolutional-residual gated recurrent hybrid model (CNN-DA-RGRU) with a two-layer attention mechanism to solve the multivariate time series prediction problem in these two stages. Specifically, the convolution module of the proposed model is used to extract the relational features among the sequences, and the two-layer attention mechanism can pay more attention to the relevant variables and give them higher weights to eliminate the irrelevant features, while the residual gated loop module is used to extract the time-varying features of the sequences, in which the residual block is used to achieve the direct connectivity to enhance the expressive power of the model, to solve the gradient explosion and vanishing scenarios, and to facilitate gradient propagation. Experiments were conducted on two public datasets using the proposed model to determine the model hyperparameters, and ablation experiments were conducted to verify the effectiveness of the model; by comparing it with several models, the proposed model was found to achieve good results in multivariate time series-forecasting tasks. Full article

Nowadays, time series data are ubiquitous, encompassing various domains like medicine, economics, energy, climate science and the Internet of Things. One crucial task in analysing these data is clustering, aiming to find patterns that indicate previously undiscovered relationships among features or specific groups of objects. In this work, we present a novel framework for the clustering of multiple multivariate time series over time that utilises multi-objective optimisation to determine the temporal clustering solution for each time point. To highlight the strength of our framework, we conduct a comparison with alternative solutions using multiple labelled real-world datasets. Our results reveal that our method not only provides better results but also enables a comparison between datasets with regard to their temporal dependencies. Full article

In the field of photovoltaic (PV) power prediction, long-term forecasting, which is more challenging than short-term forecasting, can provide more comprehensive and forward-looking guidance. Currently, significant achievements have been made in the field of short-term forecasting for PV power, but inadequate attention has been paid to long-term forecasting. Additionally, multivariate global forecasting across multiple sites and the limited historical time series data available further increase the difficulty of prediction. To address these challenges, we propose a variable–adaptive channel-independent architecture (VACI) and design a deep tree-structured multi-scale gated component named DTM block for this architecture. Subsequently, we construct a specific forecasting model called DTMGNet. Unlike channel-independent modeling and channel-dependent modeling, the VACI integrates the advantages of both and emphasizes the diversity of training data and the model’s adaptability to different variables across channels. Finally, the effectiveness of the DTM block is empirically validated using the real-world solar energy benchmark dataset. And on this dataset, the multivariate long-term forecasting performance of DTMGNet achieved state-of-the-art (SOTA) levels, particularly making significant breakthroughs in the 720-step ultra-long forecasting window, where it reduced the MSE metric below 0.2 for the first time (from 0.215 to 0.199), representing a reduction of 7.44%. Full article

Due to the inherent intermittency, variability, and randomness, photovoltaic (PV) power generation faces significant challenges in energy grid integration. To address these challenges, current research mainly focuses on developing more efficient energy management systems and prediction technologies. Through optimizing scheduling and integration in PV power generation, the stability and reliability of the power grid can be further improved. In this study, a new prediction model is introduced that combines the strengths of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms, so we call this algorithm CNN-LSTM-Attention (CLA). In addition, the Crested Porcupine Optimizer (CPO) algorithm is utilized to solve the short-term prediction problem in photovoltaic power generation. This model is abbreviated as CPO-CLA. This is the first time that the CPO algorithm has been introduced into the LSTM algorithm for parameter optimization. To effectively capture univariate and multivariate time series patterns, multiple relevant and target variables prediction patterns (MRTPPs) are employed in the CPO-CLA model. The results show that the CPO-CLA model is superior to traditional methods and recent popular models in terms of prediction accuracy and stability, especially in the 13 h timestep. The integration of attention mechanisms enables the model to adaptively focus on the most relevant historical data for future power prediction. The CPO algorithm further optimizes the LSTM network parameters, which ensures the robust generalization ability of the model. The research results are of great significance for energy generation scheduling and establishing trust in the energy market. Ultimately, it will help integrate renewable energy into the grid more reliably and efficiently. Full article

Synovial sarcoma (SS) is a rare soft-tissue tumor characterized by a monomorphic blue spindle cell histology and variable epithelial differentiation. Morphologically, SSs may be confused with other sarcomas. Systemic treatment is more effective for patients with high-risk SSs, patients with advanced disease, and younger patients. However, further studies are required to find new prognostic biomarkers. Herein, we describe the morphological, molecular, and clinical findings, using a wide immunohistochemical panel, of a series of SS cases. We studied 52 cases confirmed as SSs by morphological diagnosis and/or molecular studies. Clinical data (gender, age, tumor size, tumor location, resection margins, adjuvant treatment, recurrences, metastasis, and survival) were also retrieved for each patient. All the available H&E slides were examined by four pathologists. Three tissue microarrays (TMAs) were constructed for each of the tumors, and a wide immunohistochemical panel was performed. For time-to-event variables, survival analysis was performed using Kaplan–Meier curves and log-rank testing, or Cox regression. Statistical significance was considered at p < 0.05. The mean age of our patients was 40.33, and the median was 40.5 years. We found a predominance of males versus females (1.7:1). The most frequent morphological subtype was monophasic. TRPS1, SS18-SSX, and SSX-C-terminus were positive in 96% of cases. GLI1 expression was strong in six and focal (cytoplasmic) in twenty patients. Moreover, BCOR was expressed in more than half of SSs. Positive expression of both proteins, BCOR and GLI1, was correlated with a worse prognosis. Multivariate analysis was also performed, but only BCOR expression appeared to be significant. The combination of GLI1 and BCOR antibodies can be used to group SSs into three risk groups (low, intermediate, and high risk). We hypothesize that these findings could identify which patients would benefit from receiving adjuvant treatment and which would not. Moreover, these markers could represent therapeutic targets in advanced stages. However, further, larger series of SSs and molecular studies are necessary to corroborate our present findings. Full article

Multivariate time series modeling has been essential in sensor-based data mining tasks. However, capturing complex dynamics caused by intra-variable (temporal) and inter-variable (spatial) relationships while simultaneously taking into account evolving data distributions is a non-trivial task, which faces accumulated computational overhead and multiple temporal patterns or distribution modes. Most existing methods focus on the former direction without adaptive task-specific learning ability. To this end, we developed a holistic spatial-temporal meta-learning probabilistic inference framework, entitled ST-MeLaPI, for the efficient and versatile learning of complex dynamics. Specifically, first, a multivariate relationship recognition module is utilized to learn task-specific inter-variable dependencies. Then, a multiview meta-learning and probabilistic inference strategy was designed to learn shared parameters while enabling the fast and flexible learning of task-specific parameters for different batches. At the core are spatial dependency-oriented and temporal pattern-oriented meta-learning approximate probabilistic inference modules, which can quickly adapt to changing environments via stochastic neurons at each timestamp. Finally, a gated aggregation scheme is leveraged to realize appropriate information selection for the generative style prediction. We benchmarked our approach against state-of-the-art methods with real-world data. The experimental results demonstrate the superiority of our approach over the baselines. Full article

This study addresses the prediction of CAN bus data, a lesser-explored aspect within unsupervised anomaly detection research. We propose the Fast-Gated Attention (FGA) Transformer, a novel approach designed for accurate and efficient prediction of CAN bus data. This model utilizes a cross-attention window to optimize computational scale and feature extraction, a gated single-head attention mechanism in place of multi-head attention, and shared parameters to minimize model size. Additionally, a generalized unbiased linear attention approximation technique speeds up attention block computation. On three datasets—Car-Hacking, SynCAN, and Automotive Sensors—the FGA Transformer achieves predicted root mean square errors of 1.86 × 10⁻³, 3.03 × 10⁻³, and 30.66 × 10⁻³, with processing speeds of 2178, 2768, and 3062 frames per second, respectively. The FGA Transformer provides the best or comparable accuracy with a speed improvement ranging from 6 to 170 times over existing methods, underscoring its potential for CAN bus data prediction. Full article

This paper presents a novel multimodal recurrent model for time series forecasting leveraging LSTM architecture, with a focus on production forecasting in oil wells equipped with rod lift systems. The model is specifically designed to handle time series data with diverse types, incorporating both images and numerical data at each time step. This capability enables a comprehensive analysis over specified temporal windows. The architecture consists of distinct submodels tailored to process different data modalities. These submodels generate a unified concatenated feature vector, providing a holistic representation of the well’s operational status. This representation is further refined through a dense layer to facilitate non-linear transformation and integration. Temporal analysis forms the core of the model’s functionality, facilitated by a Long Short-Term Memory (LSTM) layer, which excels at capturing long-range dependencies in the data. Additionally, a fully connected layer with linear activation output enables one-shot multi-step forecasting, which is necessary because the input and output have different modalities. Experimental results show that the proposed multimodal model achieved the best performance in the studied cases, with a Mean Absolute Percentage Error (MAPE) of 8.2%, outperforming univariate and multivariate deep learning-based models, as well as ARIMA implementations, which yielded results with a MAPE greater than 9%. Full article

Background and aims: Management of severe thrombocytopenia poses significant challenges in patients with chronic liver disease. Here, we aimed to evaluate the first real-world European post-marketing cohort of cirrhotic patients treated with lusutrombopag, a thrombopoietin receptor agonist, verifying the efficacy and safety of the drug. Methods: In the REAl-world Lusutrombopag treatment in ITalY (REALITY) study, we collected data from consecutive cirrhotic patients treated with lusutrombopag in 19 Italian hepatology centers, mostly joined to the “Club Epatologi Ospedalieri” (CLEO). Primary and secondary efficacy endpoints were the ability of lusutrombopag to avoid platelet transfusions and to raise the platelet count to ≥50,000/μL, respectively. Treatment-associated adverse events were also collected. Results: A total of 66 patients and 73 cycles of treatment were included in the study, since 5 patients received multiple doses of lusutrombopag over time for different invasive procedures. Fourteen patients (19%) had a history of portal vein thrombosis (PVT). Lusutrombopag determined a significant increase in platelet count [from 37,000 (33,000–44,000/μL) to 58,000 (49,000–82,000), p < 0.001]. The primary endpoint was met in 84% of patients and the secondary endpoint in 74% of patients. Baseline platelet count was the only independent factor associated with response in multivariate logistic regression analysis (OR for any 1000 uL of 1.13, CI95% 1.04–1.26, p 0.01), with a good discrimination power (AUROC: 0.78). Notably, a baseline platelet count ≤ 29,000/μL was identified as the threshold for identifying patients unlikely to respond to the drug (sensitivity of 91%). Finally, de novo PVT was observed in four patients (5%), none of whom had undergone repeated treatment, and no other safety or hemorrhagic events were recorded in the entire population analyzed. Conclusions: In this first European real-world series, lusutrombopag demonstrated efficacy and safety consistent with the results of registrational studies. According to our results, patients with baseline platelet counts ≤29,000/μL are unlikely to respond to the drug. Full article

Solar energy has become a promising renewable energy source, offering significant opportunities for photovoltaic (PV) systems. Accurate and reliable PV generation forecasts are crucial for efficient grid integration and optimized system planning. However, the complexity of environmental factors, including seasonal and daily patterns, as well as social behaviors and user habits, presents significant challenges. Traditional prediction models often struggle with capturing the complex nonlinear dynamics in multivariate time series, leading to low prediction accuracy. To address this issue, this paper proposes a new PV power prediction method that considers factors such as light, air pressure, wind direction, and social behavior, assigning different weights to them to accurately extract nonlinear feature relationships. The framework integrates long short-term memory (LSTM) and gated recurrent units (GRU) to capture local time features, while bidirectional LSTM (BiLSTM) and an attention mechanism extract global spatiotemporal relationships, effectively capturing key features related to historical output. This improves the accuracy of multi-step predictions. To verify the feasibility of the method for multivariate time series, we conducted experiments using PV power prediction as a scenario and compared the results with LSTM, CNN, BiLSTM, CNN-LSTM and GRU models. The experimental results show that the proposed method outperforms these models, with a mean absolute error (MAE) of 12.133, root mean square error (RMSE) of 14.234, mean absolute percentage error (MAPE) of 2.1%, and a coefficient of determination (R²) of 0.895. These results indicate the effectiveness and potential of the method in PV prediction tasks. Full article

Background: Biomedical data are usually collections of longitudinal data assessed at certain points in time. Clinical observations assess the presences and severity of symptoms, which are the basis for the description and modeling of disease progression. Deciphering potential underlying unknowns from the distinct observation would substantially improve the understanding of pathological cascades. Hidden Markov Models (HMMs) have been successfully applied to the processing of possibly noisy continuous signals. We apply ensembles of HMMs to categorically distributed multivariate time series data, leaving space for expert domain knowledge in the prediction process. Methods: We use an ensemble of HMMs to predict the loss of free walking ability as one major clinical deterioration in the most common autosomal dominantly inherited ataxia disorder worldwide. Results: We present a prediction pipeline that processes data paired with a configuration file, enabling us to train, validate and query an ensemble of HMMs. In particular, we provide a theoretical and practical framework for multivariate time-series inference based on HMMs that includes constructing multiple HMMs, each to predict a particular observable variable. Our analysis is conducted on pseudo-data, but also on biomedical data based on Spinocerebellar ataxia type 3 disease. Conclusions: We find that the model shows promising results for the data we tested. The strength of this approach is that HMMs are well understood, probabilistic and interpretable models, setting it apart from most Deep Learning approaches. We publish all code and evaluation pseudo-data in an open-source repository. Full article