SEER compares each completed forecast with the subsequently observed trajectory, then converts the residual error into two forms of feedback.
Forecast errors can improve event-aware time series predictions
SEER uses past prediction failures to refine event retrieval and accumulate reusable causal knowledge while enforcing chronological boundaries.
Big Tech
Mingtian Tan · Palash Goyal · Mihir Parmar · Sarkar Snigdha Sarathi Das · Chun-Liang Li · Nanyun Peng · +3 more
Google Cloud AI Research · University of Virginia
Research Digest··3 min read
Tan et al.
Why this paper
From Google Cloud AI Research and University of Virginia
In one line
SEER dynamically refines event retrieval and causal knowledge using prediction errors to improve time series forecasting.
What we could check
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