Papers › AA-Forecast: Anomaly-Aware Forecast for Extreme Events

AA-Forecast: Anomaly-Aware Forecast for Extreme Events

21 Aug 2022arXiv:2208.09933archive 2025-07-28

Ashkan Farhangi, Jiang Bian, Arthur Huang, Haoyi Xiong, Jun Wang, Zhishan Guo

Time series models often deal with extreme events and anomalies, both prevalent in real-world datasets. Such models often need to provide careful probabilistic forecasting, which is vital in risk management for extreme events such as hurricanes and pandemics. However, it is challenging to automatically detect and learn to use extreme events and anomalies for large-scale datasets, which often require manual effort. Hence, we propose an anomaly-aware forecast framework that leverages the previously seen effects of anomalies to improve its prediction accuracy during and after the presence of extreme events. Specifically, the framework automatically extracts anomalies and incorporates them through an attention mechanism to increase its accuracy for future extreme events. Moreover, the framework employs a dynamic uncertainty optimization algorithm that reduces the uncertainty of forecasts in an online manner. The proposed framework demonstrated consistent superior accuracy with less uncertainty on three datasets with different varieties of anomalies over the current prediction models.

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Code

ashfarhangi/aa-forecast officialmentioned in papermentioned on GitHubpytorch report

dhamnanineha0801/AA-Forecast-Final-Project-MLP mentioned on GitHubpytorch report

1337Dylan/Time-series-Anomaly-Detection-and-Prediction- pytorch report

Buildsf409/aa-forecast-tensorflow tf report

Chasm4359/ProTS pytorchBSD-3-Clause report

GZachF/Deep-Time-Series-Forecasting pytorch report

MatFtDev149/Anomaly-Forecast-and-Detection pytorch report

PureStudios/AA-Forecast.pytorch pytorch report

ZHunter51/feature-aligned-aa-forecast pytorch report

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Tasks

Anomaly Forecasting Multivariate Time Series Forecasting Probabilistic Time Series Forecasting Time Series Time Series Analysis Time Series Anomaly Detection Time Series Forecasting Time Series Prediction Univariate Time Series Forecasting

Datasets

Introduced by this paper, per the archive.

Consumer Spendings Extreme Events > Natural Disasters > Hurricane

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Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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