Deep Coupling Network for Multivariate Time Series Forecasting
ACM Transactions on Information SystemsMultivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- and inter-series relationships among time series data. However, previous work has typically modeled intra- and inter-series relationships separately and has disregarded multi ...
Kun Yi 0001 +6 more
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Improving long-term multivariate time series forecasting with a seasonal-trend decomposition-based 2-dimensional temporal convolution dense network [PDF]
Scientific ReportsImproving the accuracy of long-term multivariate time series forecasting is important for practical applications. Various Transformer-based solutions emerging for time series forecasting.
Jianhua Hao, Fangai Liu
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A novel extreme adaptive GRU for multivariate time series forecasting [PDF]
Scientific ReportsMultivariate time series forecasting is a critical problem in many real-world scenarios. Recent advances in deep learning have significantly enhanced the ability to tackle such problems.
Yifan Zhang +3 more
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Attention-Based Models for Multivariate Time Series Forecasting: Multi-step Solar Irradiation Prediction [PDF]
HeliyonBangladesh's subtropical climate with an abundance of sunlight throughout the greater portion of the year results in increased effectiveness of solar panels.
Sadman Sakib +7 more
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Forecasting time series with multivariate copulas [PDF]
Dependence Modeling, 2015 Abstract In this paper we present a forecasting method for time series using copula-based models for multivariate time series. We study how the performance of the predictions evolves when changing the strength of the different possible dependencies, as well as the structure of the dependence.
Simard Clarence, Rémillard Bruno
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Multi-Granular Embedding and Hybrid Encoder–Decoder Architecture with Temporal Linear Regression Bypass for Sensor-Based Time Series Forecasting in Smart Infrastructure [PDF]
SensorsIn this work, we propose a robust deep encoder–decoder neural network that integrates multiple deep sequence models for time series forecasting in sensor-based applications.
Hsu-Yung Cheng +3 more
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Improving forecasting by estimating time series structural components across multiple frequencies [PDF]
, 2014 Identifying the appropriate time series model to achieve good forecasting accuracy is a challenging task. We propose a novel algorithm that aims to mitigate the importance of model selection, while increasing accuracy.
Trapero Arenas, J.R +8 more
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Evaluation of interpretability methods for multivariate time series forecasting. [PDF]
Appl Intell (Dordr), 2022 Being able to interpret a model's predictions is a crucial task in many machine learning applications. Specifically, local interpretability is important in determining why a model makes particular predictions. Despite the recent focus on interpretable Artificial Intelligence (AI), there have been few studies on local interpretability methods for time ...
Ozyegen O, Ilic I, Cevik M.
europepmc +5 more sources
Multivariate Count Data Models for Time Series Forecasting
Entropy, 2021 Count data appears in many research fields and exhibits certain features that make modeling difficult. Most popular approaches to modeling count data can be classified into observation and parameter-driven models. In this paper, we review two models from
Yuliya Shapovalova +2 more
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Multivariate Time Series Forecasting with Transfer Entropy Graph
Tsinghua Science and Technology, 2023 Multivariate Time Series (MTS) forecasting is an essential problem in many fields. Accurate forecasting results can effectively help in making decisions. To date, many MTS forecasting methods have been proposed and widely applied.
Ziheng Duan +4 more
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