Different-region Balance Method for Exploring Varying Causal Relations Between Time Series

Kaijun WANG; Yuanpeng ZENG; Zhongjian MIAO

doi:10.11999/JEIT200756

Volume 43 Issue 8

Aug. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(8): 2414-2420

Kaijun WANG, Yuanpeng ZENG, Zhongjian MIAO. Different-region Balance Method for Exploring Varying Causal Relations Between Time Series[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2414-2420. doi: 10.11999/JEIT200756

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Kaijun WANG, Yuanpeng ZENG, Zhongjian MIAO. Different-region Balance Method for Exploring Varying Causal Relations Between Time Series[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2414-2420. doi: 10.11999/JEIT200756

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Different-region Balance Method for Exploring Varying Causal Relations Between Time Series

doi: 10.11999/JEIT200756

1.
College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, China
2.
Digit Fujian Internet-of-Things Laboratory of Environmental Monitoring, Fuzhou 350117, China

Funds: The National Natural Science Foundation of China (61672157), The Natural Science Foundation of Fujian Province (2018J01778)

Received Date: 2020-08-26
Rev Recd Date: 2021-01-01

Available Online: 2021-01-07

Publish Date: 2021-08-10

Abstract

Abstract

For discovering time-varying causal relations between time series, a common method is the sliding-window method with Granger causal tests on every window. However, the method performance is sensitive to window sizes, and an unsuitable size probably leads to poor performance. The different-region balance method is proposed. The variation degree of time series in current sliding window W (called variation bound Sw) is first computed, and the degree Su in front neighbor region U of W is computed. Then a forward exploring strategy is adopted: when Su≤Sw, a different-length-region balance test measure is carried out, i.e., causal-relation tests respectively in window W, combined region W and U, and combined window W and back neighbor region V of W; when Su>Sw, it uses the above-mentioned measure where region V has the same length as region U; Finally, in each region, all the test results are synthesized to give a final result. The new method combines the results from different-length regions to reduce its sensitivity to window sizes, and guarantees the accuracy and stability of final results. The experiments on one simulated data set and four real data sets show that, the new method can discover time-varying causal relations between time series effectively, and outperforms the compared methods on the balance performance of high accuracy and stability.
- Time series,
- Time-varying causal relations,
- Granger causal test,
- Different-region balance

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References(15)

References

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