Summary
An interpretable forecasting model for building load and temperature — and the finding that, depending on encoding length, a model can reach good scores for the wrong reasons.
Authors
Wonjun Choi, Sangwon Lee (co-first authors)
Interpretable deep learning model for load and temperature forecasting: Depending on encoding length, models may be cheating on wrong answers
An interpretable forecasting model for building load and temperature — and the finding that, depending on encoding length, a model can reach good scores for the wrong reasons.