Forecast Bias Correction: A Second Order Method

TL;DR

This paper introduces a second order method for correcting forecast bias by estimating parameter and initial condition adjustments, demonstrated through experiments with the logistic differential equation.

Contribution

The paper presents a novel second order correction method for forecast bias, improving upon previous first order approaches.

Findings

Second order method effectively reduces forecast bias.

Iterative experiments show improved correction accuracy.

Comparison indicates advantages over first order methods.

Abstract

The difference between a model forecast and actual observations is called forecast bias. This bias is due to either incomplete model assumptions and/or poorly known parameter values and initial/boundary conditions. In this paper we discuss a method for estimating corrections to parameters and initial conditions that would account for the forecast bias. A set of simple experiments with the logistic ordinary differential equation is performed using an iterative version of a first order version of our method to compare with the second order version of the method.