| Budget Amount *help |
¥16,900,000 (Direct Cost: ¥13,000,000、Indirect Cost: ¥3,900,000)
Fiscal Year 2022: ¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2021: ¥5,460,000 (Direct Cost: ¥4,200,000、Indirect Cost: ¥1,260,000)
Fiscal Year 2020: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2019: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Outline of Final Research Achievements |
A state-space model describing the dynamics behind time-series data was extended to a general-purpose model using a sequential generative model of deep learning. By employing a different type of estimation method for the parameters included in the model, we succeeded in reducing the computational memory to 1/100 of that of existing methods while reducing the error to a smaller level than existing methods. We applied this method to simulation data and chaotic phenomenon data, and showed that it is also effective as a feature learning method for multidimensional time series data.
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