Data-driven prediction for volatile processes based on real option theories

Abdullah AlShelahi, Jingxing Wang, Mingdi You, Eunshin Byon and Romesh Saigal

International Journal of Production Economics, 2020, vol. 226, issue C

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, and finance. The numerical results demonstrate that the proposed model shows competitive prediction capability, compared with alternative approaches.

Keywords: Predictive modeling; Time-varying geometric brownian motion; Real options; Manufacturing (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:226:y:2020:i:c:s0925527319304384

DOI: 10.1016/j.ijpe.2019.107605

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