Explore the vast range of titles available.

Over the past decades, energy forecasting has attracted many researchers. The electrification of the modern world influences the necessity of electricity load, wind energy, and solar energy forecasting in power sectors. Energy demand increases with the increase in population. The energy has inherent characteristics like volatility and uncertainty. So, the design of accurate energy forecasting is a critical task. The electricity load, wind, and solar energy are important for maintaining the energy supply-demand equilibrium non-conventionally. Energy demand can be handled effectively using accurate load, wind, and solar energy forecasting. It helps to maintain a sustainable environment by meeting the energy requirements accurately. The limitation in the availability of sufficient data becomes a hindrance to achieving accurate energy forecasting. The transfer learning strategy supports overcoming the hindrance by transferring the knowledge from the models of similar domains where sufficient data is available for training. The present study focuses on the importance of energy forecasting, discusses the basics of transfer learning, and describes the significance of transfer learning in load forecasting, wind energy forecasting, and solar energy forecasting. It also explores the reviews of work done by various researchers in electricity load forecasting, wind energy forecasting, and solar energy forecasting. It explores how the researchers utilized the transfer learning concepts and overcame the limitations of designing accurate electricity load, wind energy, and solar energy forecasting models.

Despite its interpretability and excellence in time series forecasting, the fuzzy time series forecasting model (FTSFM) faces significant challenges when handling non-stationary time series. This paper proposes a novel hybrid non-stationary FTSFM that integrates time-variant FTSFM, Bayesian network (BN), and non-stationary fuzzy sets. We first apply first-order differencing to extract the fluctuation information of the time series while reducing non-stationarity. A novel time-variant FTSFM updating method is proposed to effectively merge historical knowledge with new observations, enhancing model stability while maintaining sensitivity to time series changes. The updating of fuzzy sets is achieved by incorporating non-stationary fuzzy sets and prediction residuals. Based on updated fuzzy sets, the system reconstructs fuzzy logical relationship groups by combining historical and new data. This approach implements dynamic quantitative modeling of fuzzy relationships between historical and predicted moments, integrating valuable historical temporal fuzzy patterns with emerging temporal fuzzy characteristics. This paper further develops an adaptive BN structure learning method with an adaptive scoring function to update temporal dependence relationships between any two moments while building upon existing dependence relationships. Experimental results indicate that the proposed model significantly outperforms benchmark algorithms.

Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN–LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN–PSO, KNN, ANN–LM with two hidden layers and bootstrap aggregation models.

This study aims to develop a predictive and reliable data-driven model for forecasting the fluid production (oil, gas, and water) of existing wells and future infill wells for CO2-enhanced oil recovery (EOR) and CO2 storage projects. Several models were investigated, such as auto-regressive (AR), multilayer perceptron (MLP), and long short-term memory (LSTM) networks. The models were trained based on static and dynamic parameters and daily fluid production while considering the inverse distance of neighboring wells. The developed models were evaluated using walk-forward validation and compared based on the quality metrics, span, and variation in the forecasting horizon. The AR model demonstrates a convincing generalization performance across various time series datasets with a long but varied forecasting horizon across eight wells. The LSTM model has a shorter forecasting horizon but strong generalizability and robustness in forecasting horizon consistency. MLP has the shortest and most varied forecasting horizon compared to the other models. The LSTM model exhibits promising performance in forecasting the fluid production of future infill wells when the model is developed from an existing well with similar features to an infill well. This study offers an alternative to the physics-driven model when traditional modeling is costly and laborious.

Realizing carbon neutral energy generation creates the challenge of accurately predicting time‐series generation data for long‐term capacity planning and for short‐term operational decisions. The key challenges for adopting data‐driven decision‐making, specifically predictive analytics, can be attributed to data volume and velocity. Data volume poses challenges for data storage and retrieval. Data velocity poses challenges for processing the data near real time for operational decisions or for capacity building. This manuscript proposes a novel prediction method to tackle the above two challenges by using an event‐based prediction in place of traditional time series prediction methods. The central concept is to extract meaningful information, denoted by events, from time‐series data and use these events for predictive analysis. These extracted events retain the information required for predictive analytics while significantly reducing the volume of the velocity of data; consequently, a series of events present the information at a glance, effectively enabling data‐driven decision‐making. This method is applied to a data set consisting of six years of historical wind power capacity factor and temperature measurements. Deploying five deep learning models, a comparison is drawn between classical time‐series predictions and series of events predictions based on computational time and several error metrics. The computational analysis results are presented in graphical format and a comparative discussion is drawn on the prediction results. The results indicate that the proposed method obtains the same or better prediction accuracy while significantly reducing computational time and data volume.

Multivariate time series forecasting is essential in various fields, including healthcare and traffic management, but it is a challenging task due to the strong dynamics in both intra-channel relations (temporal patterns within individual variables) and inter-channel relations (the relationships between variables), which can evolve over time with abrupt changes. This paper proposes ERAN (Evolving Relational Attention Network), a framework for multivariate time series forecasting, that is capable to capture such dynamics of these relations. On the one hand, ERAN represents inter-channel relations with a graph which evolves over time, modeled using a recurrent neural network. On the other hand, ERAN represents the intra-channel relations using a temporal attentional convolution, which captures the local temporal dependencies adaptively with the input data. The elvoving graph structure and the temporal attentional convolution are intergrated in a unified model to capture both types of relations. The model is experimented on a large number of real-life datasets including traffic flows, energy consumption, and COVID-19 transmission data. The experimental results show a significant improvement over the state-of-the-art methods in multivariate time series forecasting particularly for non-stationary data.

Time series data analysis is crucial for real-world applications. While deep learning has advanced in this field, it still faces challenges, such as limited or poor-quality data. In areas like computer vision, data augmentation has been widely used and highly effective in addressing similar issues. However, these techniques are not as commonly explored or applied in the time series domain. This paper addresses the gap by evaluating basic data augmentation techniques using MLP, CNN, and Transformer architectures, prioritized for their alignment with state-of-the-art trends in time series analysis rather than traditional RNN-based methods. The goal is to expand the use of data augmentation in time series analysis. The paper proposed EMixup, which adapts the Mixup method from image processing to time series data. This adaptation involves mixing samples while aiming to maintain the data's temporal structure and integrating target contributions into the loss function. Empirical studies show that EMixup improves the performance of time series models across various architectures (improving 23/24 forecasting cases and 12/24 classification cases). It demonstrates broad applicability and strong results in tasks like forecasting and classification, highlighting its potential utility across diverse time series applications.

Yanyan Chen,1,2,* Ziqi Song,1,* Ruihua Di,1,* Qing Zhao,1,2 Jia Liu,1 Haobin Song,1,2 Jingya Wang,1 Yingnan Chen3 1Department of Laboratory Medicine, Baoding Hospital of Beijing Children’s Hospital, Capital Medical University, Baoding, Hebei, 071000, People’s Republic of China; 2Hebei Key Laboratory of Infectious Diseases Pathogenesis and Precise Diagnosis and Treatment, Baoding Hebei, 071000, People’s Republic of China; 3The Third Special Care Hospital for Disabled Soldiers of Hebei Province, Baoding, 071000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yingnan Chen, Email yingnan090905@163.comBackground: Pediatric Escherichia coli infections are a major cause of morbidity, and increasing antimicrobial resistance (AMR) complicates empirical treatment. Long-term local surveillance combined with trend forecasting may support rational antimicrobial use and stewardship in northern China.Methods: We retrospectively analyzed 2021 pediatric E. coli isolates collected at Baoding Hospital of Beijing Children’s Hospital, Capital Medical University (2018– 2024), calculated annual resistance rates to 14 commonly used antibiotics and the prevalence of extended-spectrum β-lactamase (ESBL) producers, defined an overall resistance indicator as the mean annual resistance across these agents, and used χ2-tests, linear regression, and autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models to evaluate temporal trends and generate exploratory forecasts for 2025– 2027.Results: Nearly half of isolates were from children < 2 years and about two-thirds from boys; pus, sputum, and urine were the predominant specimen types. Resistance to ampicillin and trimethoprim–sulfamethoxazole remained high, whereas resistance to several β-lactams, including ampicillin/sulbactam and third-generation cephalosporins, declined significantly over time. ESBL-producing isolates accounted for 45.93% of all strains, with annual detection rates > 50% in 2018– 2020 and around 40% thereafter, while carbapenems and amikacin maintained very low resistance rates. Both ARIMA and LSTM models suggested a modest further decline in the overall resistance indicator through 2027, with LSTM showing slightly better fit and lower prediction errors.Conclusion: Pediatric E. coli isolates in our center exhibited high resistance to several common oral agents but encouraging declines in ESBL prevalence and cephalosporin resistance, likely reflecting local antimicrobial stewardship. Exploratory AI-based time-series models may help anticipate resistance trajectories and support pediatric antibiotic policies and stewardship, although forecasts from a short annual series should be interpreted cautiously.Keywords: Escherichia coli, pediatric infections, antimicrobial resistance, extended-spectrum β-lactamase, ESBL, time-series forecasting, LSTM

Predicting time series is of great importance in various scientific and engineering fields. However, in the context of limited and noisy data, accurately predicting the dynamics of all variables in a high‐dimensional system is a challenging task due to their nonlinearity and complex interactions. This study introduces the Delayformer framework for simultaneously predicting the dynamics of all variables by developing a novel multivariate spatiotemporal information (mvSTI) transformation that makes each observed variable into a delay‐embedded state (vector) and further cross‐learns those states from different variables. From a dynamical systems viewpoint, Delayformer predicts system states rather than individual variables, thus theoretically and computationally overcoming such nonlinearity and cross‐interaction problems. Specifically, it first utilizes a single shared Visual Transformer (ViT) encoder to cross‐represent dynamical states from observed variables in a delay‐embedded form and then employs distinct linear decoders for predicting next states, i.e., equivalently predicting all original variables in parallel. By leveraging the theoretical foundations of delay embedding theory and the representational capabilities of Transformers, Delayformer outperforms current state‐of‐the‐art methods in forecasting tasks on both synthetic and real‐world datasets. Furthermore, the potential of Delayformer as a foundational time‐series model is demonstrated through cross‐domain forecasting tasks, highlighting its broad applicability across various scenarios. Delayformer introduces a multivariate spatiotemporal transformation (mvSTI) that converts observed variables into delay‐embedded states and cross‐learns their dynamics using a shared Vision Transformer encoder. This approach, grounded in dynamical systems theory, simultaneously predicts all variables in high‐dimensional systems, outperforming state‐of‐the‐art methods on synthetic and real‐world benchmarks and demonstrating strong potential as a foundational time‐series model.

Forecasting financial time series is challenging due to their intrinsic nonlinearity, high volatility, and complex dependencies across temporal scales. This study introduces MSGformer, a novel hybrid architecture that integrates multi-scale graph neural networks (MSGNet) with Transformer encoders to capture both local temporal fluctuations and long-term global trends in high-frequency financial data. The MSGNet module constructs multi-scale representations using adaptive graph convolutions and intra-sequence attention, while the Transformer component enhances long-range dependency modeling via multi-head self-attention. We evaluate MSGformer on minute-level stock index data from the Chinese A-share market, including CSI 300, SSE 50, CSI 500, and SSE Composite indices. Extensive experiments demonstrate that MSGformer significantly outperforms state-of-the-art baselines (e.g., Transformer, PatchTST, Autoformer) in terms of MAE, RMSE, MAPE, and R2. The results confirm that the proposed hybrid model achieves superior prediction accuracy, robustness, and generalization across various forecasting horizons, providing an effective solution for real-world financial decision-making and risk assessment.