# An Alternative Data-Driven Prediction Approach Based on Real Option Theories

## TL;DR

This paper introduces a novel prediction model for volatile time series data by integrating a time-varying Geometric Brownian Motion with real option pricing, outperforming traditional linear models in accuracy.

## Contribution

The paper develops a flexible, non-linear prediction model based on real option theory, addressing volatility and non-linearity in time series forecasting.

## Key findings

- Demonstrates improved prediction accuracy over traditional models.
- Applicable across manufacturing, finance, and environmental datasets.
- Shows competitive performance in real-world scenarios.

## Abstract

This paper presents a new prediction model for time series data by integrating a time-varying Geometric Brownian Motion model with a pricing mechanism used in financial engineering. Typical time series models such as Auto-Regressive Integrated Moving Average assumes a linear correlation structure in time series data. When a stochastic process is highly volatile, such an assumption can be easily violated, leading to inaccurate predictions. We develop a new prediction model that can flexibly characterize a time-varying volatile process without assuming linearity. We formulate the prediction problem as an optimization problem with unequal overestimation and underestimation costs. Based on real option theories developed in finance, we solve the optimization problem and obtain a predicted value, which can minimize the expected prediction cost. We evaluate the proposed approach using multiple datasets obtained from real-life applications including manufacturing, finance, and environment. The numerical results demonstrate that the proposed model shows competitive prediction capability, compared with alternative approaches.