Learning Predictive Generalizations for Multiple Streams: An Incremental Algorithm
نویسندگان
چکیده
We present an approach to learning complex dependencies among multiple streams of time-series data incrementally. Given a set of input streams that contain categorical values that change over time, we characterize recurring structure with a set of dependency rules that can be used to predict stream values in the future. These rules are general in the sense of ignoring noisy values in streams.
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