Time series data is a sequence of data points over time that allows to understand the evolution of a system by analyzing the trends and influencing variables. It serves as the foundation for time series forecasting, where historical patterns, trends, and seasonal variations are analyzed to make informed predictions about future values. Time series forecasting plays a crucial role in the utility sector by enabling accurate demand prediction, optimizing energy distribution, and ensuring efficient resource management, helping providers balance supply and demand while minimizing operational costs and outages.
A significant challenge in time series forecasting lies in the behavioral heterogeneity of the data. Each time series exhibits unique characteristics, making a one-size-fits-all approach inadequate. With respect to the utility sector, this is particularly evident in data from residential, agricultural and industrial zones, where distinct consumption patterns necessitate different forecasting models. This research seeks to address this challenge by determining the best-suited forecasting model that accounts for the unique characteristics of the time series data.
This thesis aims to evaluate time series forecasting approaches for water and heat utility networks and identify the most suitable forecasting models for different time series data with unique demand and usage patterns. The goal is to classify time series data into distinct categories by their characteristics to find and apply the best forecasting model for each. To achieve this, we will leverage statistical methods, Machine Learning (ML), and Deep Learning (DL) techniques, which offer advanced capabilities for handling non-linearities, automatically extracting features, managing large datasets, and capturing complex dependencies. Forecasting approaches are chosen based on their nature of input and the nature in which the data is processed. Popular approaches include statistical methods, frequency-aware techniques [1], machine learning algorithms [2], recurrent neural networks [3], transformer-based architectures [4], and emerging foundation models [5]. The research will involve categorizing time series data, training and testing different ML and DL models, and evaluating their performance based on various metrics to determine the most suitable model for each category.
The anticipated outcome of this thesis is the creation of a robust framework for classifying time series data by their unique characteristics and identifying the most suitable forecasting models for each category. This research aims to:
- Classify time series data based on distinct attributes.
- Conduct a comprehensive evaluation of various ML and DL models tailored to each data category.
- Improve the precision of demand forecasts for water and heat networks through customized prediction models.
By determining models tailored to the specific characteristics of water and heat consumption data, this research proposal aims to identify the most suitable approach for a given time series and evaluate how they perform relative to each other leading to significantly improving the accuracy of future demand predictions, facilitating better resource management and infrastructure planning.
References:
[1] P. C. Young, D. J. Pedregal, and W. Tych, “Dynamic harmonic regression,” J Forecast, vol. 18, no. 6, pp. 369–394, Nov. 1999, doi: 10.1002/(SICI)1099-131X(199911)18:6<369::AID-FOR748>3.0.CO;2-K.
[2] T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 13-17-August-2016, pp. 785–794, Mar. 2016, doi: 10.1145/2939672.2939785.
[3] M. Beck et al., “xLSTM: Extended Long Short-Term Memory,” May 2024, Accessed: Jun. 16, 2025. [Online]. Available: https://arxiv.org/pdf/2405.04517
[4] Y. Nie, N. H. Nguyen, P. Sinthong, and J. Kalagnanam, “A Time Series is Worth 64 Words: Long-term Forecasting with Transformers,” 11th International Conference on Learning Representations, ICLR 2023, Nov. 2022, Accessed: Jun. 16, 2025. [Online]. Available: https://arxiv.org/pdf/2211.14730
[5] V. Ekambaram et al., “Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series,” Jan. 2024. Available: https://arxiv.org/pdf/2401.03955
This thesis is part of the “UtilityTwin” project.