The paper introduces a novel framework called Motion Code for robust time series classification and forecasting. The key ideas are:
Modeling each time series as an instance of an underlying stochastic process, which allows capturing the data dependence across timestamps and separating noise from true signals.
Handling multiple distinct dynamics in the data by assigning a signature vector (motion code) to each dynamical model and optimizing them jointly.
Introducing the concept of the most informative timestamps, which are a small subset of timestamps that minimizes the mismatch between the original data and the information reconstructed using only this subset. This allows an interpretable feature for visualizing the core dynamics.
The final Motion Code model can jointly learn across multiple time series collections, enabling simultaneous classification and forecasting without the need for separate models.
Extensive experiments on noisy sensor and device time series data demonstrate Motion Code's competitiveness against state-of-the-art time series classification and forecasting benchmarks, as well as its ability to handle uneven length and missing data.
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arxiv.org
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by Chandrajit B... ב- arxiv.org 04-25-2024
https://arxiv.org/pdf/2402.14081.pdfשאלות מעמיקות