# The uncertainty estimation of feature-based forecast combinations

## TL;DR

This paper presents a feature-based framework for improving the estimation of forecast uncertainty in time series, enhancing decision-making in supply chain management by providing more reliable interval forecasts.

## Contribution

It introduces a novel feature-based approach with an optimal threshold algorithm and weight mechanism for better model selection and combination in interval forecasting.

## Key findings

- Significantly outperforms benchmark models in point and interval forecasts
- Effective in large-scale time series from M4 competition
- Improves decision-making in inventory and supply chain management

## Abstract

Forecasting is an indispensable element of operational research (OR) and an important aid to planning. The accurate estimation of the forecast uncertainty facilitates several operations management activities, predominantly in supporting decisions in inventory and supply chain management and effectively setting safety stocks. In this paper, we introduce a feature-based framework, which links the relationship between time series features and the interval forecasting performance into providing reliable interval forecasts. We propose an optimal threshold ratio searching algorithm and a new weight determination mechanism for selecting an appropriate subset of models and assigning combination weights for each time series tailored to the observed features. We evaluate our approach using a large set of time series from the M4 competition. Our experiments show that our approach significantly outperforms a wide range of benchmark models, both in terms of point forecasts as well as prediction intervals.