Robust LSTM With tuned-PSO and bifold-attention mechanism for analyzing multivariate time-series
The need for accurate time-series results is badly demanding. LSTM has been applied for
forecasting time series, which is generated when variables are observed at discrete and …
forecasting time series, which is generated when variables are observed at discrete and …
End-to-end multivariate time series classification via hybrid deep learning architectures
Deep learning has revolutionized many areas, including time series data mining.
Multivariate time series classification (MTSC) remained to be a well-known problem in the …
Multivariate time series classification (MTSC) remained to be a well-known problem in the …
MR-Transformer: Multiresolution Transformer for Multivariate Time Series Prediction
Multivariate time series (MTS) prediction has been studied broadly, which is widely applied
in real-world applications. Recently, transformer-based methods have shown the potential in …
in real-world applications. Recently, transformer-based methods have shown the potential in …
A dual-stage attention-based Bi-LSTM network for multivariate time series prediction
In the context of the big data era, time series data present the characteristics of high
dimensionality and nonlinearity, which bring great challenges to the prediction of …
dimensionality and nonlinearity, which bring great challenges to the prediction of …
Trid-mae: A generic pre-trained model for multivariate time series with missing values
Multivariate time series (MTS) is a universal data type related to various real-world
applications. Data imputation methods are widely used in MTS applications to deal with the …
applications. Data imputation methods are widely used in MTS applications to deal with the …
A tensor-based deep LSTM forecasting model capturing the intrinsic connection in multivariate time series
Z Fu, Y Wu, X Liu - Applied Intelligence, 2023 - Springer
Multivariate time series forecasting has many practical applications in a variety of domains
such as commerce, weather, environment, and transportation. There exist so many methods …
such as commerce, weather, environment, and transportation. There exist so many methods …
A hybrid method with adaptive sub-series clustering and attention-based stacked residual LSTMs for multivariate time series forecasting
F Liu, Y Lu, M Cai - IEEE Access, 2020 - ieeexplore.ieee.org
Multivariate Time Series Forecasting (MTSF) has recently emerged its growing importance
in many industries. However, how to reduce the influence of the noise components existing …
in many industries. However, how to reduce the influence of the noise components existing …
Temporal self-attention-based Conv-LSTM network for multivariate time series prediction
E Fu, Y Zhang, F Yang, S Wang - Neurocomputing, 2022 - Elsevier
Time series play an important role in many fields, such as industrial control, automated
monitoring, and weather forecasting. Because there is often more than one variable in reality …
monitoring, and weather forecasting. Because there is often more than one variable in reality …
An ensemble model based on adaptive noise reducer and over-fitting prevention LSTM for multivariate time series forecasting
F Liu, M Cai, L Wang, Y Lu - Ieee Access, 2019 - ieeexplore.ieee.org
Multivariate time series forecasting recently has received extensive attention with its wide
application in finance, transportation, environment, and so on. However, few of the currently …
application in finance, transportation, environment, and so on. However, few of the currently …
Prediction for Time Series with CNN and LSTM
Time series data exist in various systems and affect the following management and control,
in which real time series data sets are often composed of multiple variables. For predicting …
in which real time series data sets are often composed of multiple variables. For predicting …
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