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What is Multivariate Forecasting? Multivariate time series forecasting considers multiple variables as input features to predict the target variable. In our case, this could involve using not only the historical power consumption data but also other relevant variables like temperature, humidity, or time of day.
Nov 11, 2023
Apr 22, 2024 · Abstract:Multivariate time series forecasting plays a crucial role in various fields such as finance, traffic management, energy, and healthcare.
A novel extreme adaptive GRU for multivariate time series forecasting
www.nature.com › ... › articles
Feb 5, 2024 · The eGRU is designed to effectively learn both normal and extreme event patterns within time series data. Furthermore, we introduce a time series data ...
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Feb 17, 2024 · Multivariate forecasting entails utilizing multiple time-dependent variables to generate predictions. This forecasting approach incorporates historical data ...
Oct 9, 2023 · Multivariate Time Series (MTS) widely exists in real-word complex systems, such as traffic and energy systems, making their forecasting crucial for ...
Feb 12, 2024 · Multivariate forecasting dives deep into the complex web of interconnected variables, painting a richer picture of what's to come.
Jan 19, 2024 · Improving the accuracy of long-term multivariate time series forecasting is important for practical applications. Various Transformer-based solutions ...
Nov 25, 2023 · We propose a long-term time-series forecasting framework called Multi-factor Separation Evolutionary Spatial–Temporal Graph Neural Networks (MSE-STGNN).
Oct 7, 2023 · The goal is indeed to make out of sample predictions for the next few months. The main goals is to understand which products will perform well and which will ...
Mar 20, 2024 · In multivariate time series forecasting, we have time series for both the target and dependent variables, commonly known as predictor variables.
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