Improving the accuracy of long-term multivariate time series forecasting is important for practical applications. Various Transformer-based solutions emerging for time series forecasting. Recently, some studies have verified that the most Transformer-based methods are outperformed by simple linear models in...
Time Series Analysis on Univariate and Multivariate Variables: A Comprehensive Surveydoi:10.1007/978-981-15-5397-4_13Time series analysis and forecasting have become an active research area for a couple of years in various domains like signal processing, weather forecasting, earthquake prediction, ...
Multivariate time series (MTS) forecasting endeavors to adeptly model the dynamic evolution of multiple variables over time from their historical records, thereby facilitating the accurate prediction of future values. This task holds significant importance in various applications1. The advent of deep learn...
Tang, Dsanet: Dual self-attention network for multivariate time series forecasting, in: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019, pp. 2129–2132. Google Scholar [24] Malhotra P., Ramakrishnan A., Anand G., Vig L., Agarwal P., Shroff ...
Time series forecasting using a hybrid Arima and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0. Article MATH Google Scholar Download references Funding Open Access funding enabled and organized by Projekt DEAL. No funding was received to ...
Graph Time-series Modeling in Deep Learning: A Survey Time-series and graphs have been extensively studied for their ubiquitous existence in numerous domains.Both topics have been separately explored in the fi... H Chen,H Eldardiry - 《Acm Transactions on Knowledge Discovery from Data》 被引量...
Multivariate Time Series (MTS) forecasting is a fundamental task with numerous real-world applications, such as transportation, climate, and epidemiology. While a myriad of powerful deep learning models have been developed for this task, few works have explored the robustness of MTS forecasting models...
deep learning has garnered significant attention for the modelling of complex time series data, mitigating the need for manual feature engineering and model design (Torres et al., 2021).Table 2presents an overview of existing studies related to multivariate time series forecasting in the field of ...
Numerical weather prediction (NWP) fields are used to linearise and act as a prior to constrain the retrieval. These were acquired from the European Centre for Medium-range Weather Forecasting (ECMWF) re-analysis, generated consistently with a single version of the atmospheric general circulation mo...
(MIMO) methods. To fill this gap, we present a new methodology for forecasting high-dimensional non-stationary time series called MO-ENSFTS (multiple output embedding non-stationary fuzzy time series). MO-ENSFTS is a first-order MIMO multivariate model. We apply a combination of data embedding...