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New loss function APAL improves time-series forecasting for peak prediction

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new loss function called Asymmetric Peak-Aware Loss (APAL) designed to improve time-series forecasting, particularly for applications where under-prediction carries higher risks than over-prediction. APAL addresses the limitations of standard symmetric objectives like MSE and MAE by penalizing under-predictions more severely and increasing the focus on peak prediction regions during training. The proposed method also includes a new evaluation protocol to better assess peak-critical forecasting performance, complementing traditional metrics with tail error and peak-specific measures. AI

IMPACT This new loss function could lead to more reliable AI-driven forecasting in critical operational scenarios, such as demand prediction for resource allocation.

RANK_REASON The cluster contains a research paper detailing a new methodology for time series forecasting.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New loss function APAL improves time-series forecasting for peak prediction

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The cluster contains a research paper detailing a new methodology for time series forecasting.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Theivaprakasham Hari, Yanan Xin, Winnie Daamen, Serge Paul Hoogendoorn, Sascha Hoogendoorn-Lanser ·

    Asymmetric Peak-Aware Loss for Peak-Critical Time Series Forecasting

    arXiv:2607.14871v1 Announce Type: cross Abstract: In many operational time-series forecasting applications, such as crowd demand forecasting, the risk related to under-prediction is substantially higher than that of over-prediction. Accurate prediction of rare demand spikes plays…

  2. arXiv cs.LG TIER_1 English(EN) · Sascha Hoogendoorn-Lanser ·

    Asymmetric Peak-Aware Loss for Peak-Critical Time Series Forecasting

    In many operational time-series forecasting applications, such as crowd demand forecasting, the risk related to under-prediction is substantially higher than that of over-prediction. Accurate prediction of rare demand spikes plays a critical role in downstream tasks. Yet most tim…

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