Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection
Understanding the causal interaction of time series variables can contribute to time series data analysis for many real-world applications, such as climate forecasting and extreme weather alerts. However, causal relationships are difficult to be fully observed in real-world complex settings, such as...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
08.08.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Understanding the causal interaction of time series variables can contribute
to time series data analysis for many real-world applications, such as climate
forecasting and extreme weather alerts. However, causal relationships are
difficult to be fully observed in real-world complex settings, such as
spatial-temporal data from deployed sensor networks. Therefore, to capture
fine-grained causal relations among spatial-temporal variables for further a
more accurate and reliable time series analysis, we first design a conceptual
fine-grained causal model named TBN Granger Causality, which adds
time-respecting Bayesian Networks to the previous time-lagged Neural Granger
Causality to offset the instantaneous effects. Second, we propose an end-to-end
deep generative model called TacSas, which discovers TBN Granger Causality in a
generative manner to help forecast time series data and detect possible
anomalies during the forecast. For evaluations, besides the causality discovery
benchmark Lorenz-96, we also test TacSas on climate benchmark ERA5 for climate
forecasting and the extreme weather benchmark of NOAA for extreme weather
alerts. |
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DOI: | 10.48550/arxiv.2408.04254 |