# Temporal Encoding Strategies for Energy Time Series Prediction

arXiv: https://arxiv.org/abs/2503.15456 · HTML: https://mlsanity.com/paper/2503.15456/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [A Multi-Horizon Quantile Recurrent Forecaster](https://mlsanity.com/paper/1711.11053/) (2017)
- [Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges](https://mlsanity.com/paper/1802.04117/) (2018)
- [Deep Learning for Time Series Forecasting: The Electric Load Case](https://mlsanity.com/paper/1907.09207/) (2019)
- [BehaveFormer: A Framework with Spatio-Temporal Dual Attention Transformers for IMU enhanced Keystroke Dynamics](https://mlsanity.com/paper/2307.11000/) (2023)

## Built on by (most cited first, 4 of 4)

- [Resource Allocation in Hybrid Radio-Optical IoT Networks using GNN with Multi-task Learning](https://mlsanity.com/paper/2511.07428/) (2025)
- [TabGRU: An Enhanced Design for Urban Rainfall Intensity Estimation Using Commercial Microwave Links](https://mlsanity.com/paper/2512.02465/) (2025)
- [SweetDeep: A Wearable AI Solution for Real-Time Non-Invasive Diabetes Screening](https://mlsanity.com/paper/2512.03471/) (2025)
- [Transformer-based Diffusion models for Hydrological Time Series Probabilistic Imputation and Forecasting](https://mlsanity.com/paper/2607.21200/) (2026)

## Abstract (arXiv)

In contemporary power systems, energy consumption prediction plays a crucial role in maintaining grid stability and resource allocation enabling power companies to minimize energy waste and avoid overloading the grid. While there are several research works on energy optimization, they often fail to address the complexities of real-time fluctuations and the cyclic pattern of energy consumption. This work proposes a novel approach to enhance the accuracy of predictive models by employing sinusoidal encoding on periodic features of time-series data. To demonstrate the increase in performance, several statistical and ensemble machine learning models were trained on an energy demand dataset, using the proposed sinusoidal encoding. The performance of these models was then benchmarked against identical models trained on traditional encoding methods. The results demonstrated a 12.6% improvement of Root Mean Squared Error (from 0.5497 to 0.4802) and a 7.8% increase in the R^2 score (from 0.7530 to 0.8118), indicating that the proposed encoding better captures the cyclic nature of temporal patterns than traditional methods. The proposed methodology significantly improves prediction accuracy while maintaining computational efficiency, making it suitable for real-time applications in smart grid systems.