Diffusion-based Time Series Forecasting for Sewerage Systems

TL;DR

This paper presents a diffusion-based deep learning model for sewerage system time series forecasting, incorporating conformal inference to produce reliable probabilistic predictions during extreme weather events.

Contribution

It introduces a novel diffusion-based model combined with conformal inference for accurate and reliable sewerage system forecasting under challenging conditions.

Findings

Model achieves high accuracy in real sewerage data

Prediction intervals are statistically reliable

Performs well during severe weather events

Abstract

We introduce a novel deep learning approach that harnesses the power of generative artificial intelligence to enhance the accuracy of contextual forecasting in sewerage systems. By developing a diffusion-based model that processes multivariate time series data, our system excels at capturing complex correlations across diverse environmental signals, enabling robust predictions even during extreme weather events. To strengthen the model's reliability, we further calibrate its predictions with a conformal inference technique, tailored for probabilistic time series data, ensuring that the resulting prediction intervals are statistically reliable and cover the true target values with a desired confidence level. Our empirical tests on real sewerage system data confirm the model's exceptional capability to deliver reliable contextual predictions, maintaining accuracy even under severe weather conditions.