A multiscale model for multivariate time series forecasting [PDF]
Scientific ReportsTransformer based models for time-series forecasting have shown promising performance and during the past few years different Transformer variants have been proposed in time-series forecasting domain.
Vahid Naghashi +2 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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Spatial-Temporal Convolutional Transformer Network for Multivariate Time Series Forecasting [PDF]
Sensors, 2022 Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies make this problem challenging.
Lei Huang +3 more
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MixNet: A scale-adaptive method for multivariate time series forecasting. [PDF]
PLoS ONETime series forecasting is a critical task with widespread applications in industrial domains and daily life, including weather prediction, long-term energy consumption planning, and marketing analysis.
Xinhan Wang, Bowen Zhao
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Multivariate Count Data Models for Time Series Forecasting [PDF]
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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Multiview Spatial-Temporal Meta-Learning for Multivariate Time Series Forecasting [PDF]
SensorsMultivariate 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 ...
Liang Zhang +3 more
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Pre-trained multi-scale RWKV-GCN for multivariate time series forecasting [PDF]
Scientific ReportsMultivariate time series forecasting faces two key challenges: capturing intra-series temporal dependencies and inter-series spatial dependencies. However, heterogeneous cross-scale correlations and noise from unrelated series may obscure temporal ...
Jianhua Hao, Fangai Liu, Weiwei Zhang
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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.
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VCformer: Variable-Centric Multi-Scale Transformer for Multivariate Time Series Forecasting [PDF]
SensorsMultivariate time series forecasting is crucial for numerous practical applications ranging from financial markets to climate monitoring. Traditional multivariate time series forecasting methods primarily adopt a time-centric modeling paradigm, applying ...
Junyu Zhu +6 more
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Consistency regularization for few shot multivariate time series forecasting [PDF]
Scientific ReportsMultivariate time series forecasting aims to accurately predict future trends by capturing and analyzing various features of the time series. Adequate training data are crucial for ensuring the model’s generalizability.
Yumei She +5 more
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