# A Hybrid PCA-PR-Seq2Seq-Adam-LSTM Framework for Time-Series Power Outage Prediction

arXiv: https://arxiv.org/abs/2509.16743 · HTML: https://mlsanity.com/paper/2509.16743/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 57 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News](https://mlsanity.com/paper/1912.10806/) (2019)
- [Real-Time Prediction of the Duration of Distribution System Outages](https://mlsanity.com/paper/1804.01189/) (2018)
- [Perturbed Decision-Focused Learning for Modeling Strategic Energy Storage](https://mlsanity.com/paper/2406.17085/) (2024)
- [End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning](https://mlsanity.com/paper/2109.00741/) (2021)
- [Transferable Energy Storage Bidder](https://mlsanity.com/paper/2301.01233/) (2023)

## Built on by (most cited first, 1 of 1)

- [Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting](https://mlsanity.com/paper/2604.24788/) (2026)

## Abstract (arXiv)

Accurately forecasting power outages is a complex task influenced by diverse factors such as weather conditions [1], vegetation, wildlife, and load fluctuations. These factors introduce substantial variability and noise into outage data, making reliable prediction challenging. Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN), are particularly effective for modeling nonlinear and dynamic time-series data, with proven applications in stock price forecasting [2], energy demand prediction, demand response [3], and traffic flow management [4]. This paper introduces a hybrid deep learning framework, termed PCA-PR-Seq2Seq-Adam-LSTM, that integrates Principal Component Analysis (PCA), Poisson Regression (PR), a Sequence-to-Sequence (Seq2Seq) architecture, and an Adam-optimized LSTM. PCA is employed to reduce dimensionality and stabilize data variance, while Poisson Regression effectively models discrete outage events. The Seq2Seq-Adam-LSTM component enhances temporal feature learning through efficient gradient optimization and long-term dependency capture. The framework is evaluated using real-world outage records from Michigan, and results indicate that the proposed approach significantly improves forecasting accuracy and robustness compared to existing methods.