ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information

TL;DR

ProbPNN is a deep learning approach that explicitly incorporates calendar-driven periodicities using statistical methods, improving probabilistic forecast accuracy and efficiency over existing methods.

Contribution

This paper introduces a novel deep learning method that combines statistical calendar-based features with neural networks for improved probabilistic time series forecasting.

Findings

ProbPNN outperforms state-of-the-art benchmarks in nCRPS and nPL metrics.

Incorporating statistical components enhances forecast accuracy.

ProbPNN requires less computational resources than comparable methods.

Abstract

Probabilistic forecasts are essential for various downstream applications such as business development, traffic planning, and electrical grid balancing. Many of these probabilistic forecasts are performed on time series data that contain calendar-driven periodicities. However, existing probabilistic forecasting methods do not explicitly take these periodicities into account. Therefore, in the present paper, we introduce a deep learning-based method that considers these calendar-driven periodicities explicitly. The present paper, thus, has a twofold contribution: First, we apply statistical methods that use calendar-driven prior knowledge to create rolling statistics and combine them with neural networks to provide better probabilistic forecasts. Second, we benchmark ProbPNN with state-of-the-art benchmarks by comparing the achieved normalised continuous ranked probability score (nCRPS) and normalised Pinball Loss (nPL) on two data sets containing in total more than 1000 time series. The results of the benchmarks show that using statistical forecasting components improves the probabilistic forecast performance and that ProbPNN outperforms other deep learning forecasting methods whilst requiring less computation costs.