The Markov-exponential grey model for efficient forecasting management

Lu, Shin‐Li; Tsai, Chen‐Fang

doi:10.3233/ida-150250

Cited by 6 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Markov procedure is a kind of stochastic process in which system communicates with a specifc probability of transitioning from a state to the other [3]. It has been applied widely in a host of real applications [26]. Te state of future time t n+1 is solely connected to current state at time t n and is not associated with any past states [12].…”

Section: Grey-markov Modelmentioning

confidence: 99%

Predicting Spacecraft Telemetry Data by Using Grey–Markov Model with Sliding Window and Particle Swarm Optimization

Ren

Feng

Gao

et al. 2023

Journal of Mathematics

View full text Add to dashboard Cite

Predicting telemetry data is vital for the proper operation of orbiting spacecraft. The Grey–Markov model with sliding window (GMSW) combines Grey model (GM (1, 1)) and Markov chain forecast model, which allows it to describe the fluctuation of telemetry data. However, the Grey–Markov model with sliding window does not provide better predictions of telemetry series with the pseudo-periodic phenomenon. To overcome this drawback, we improved the GMSW model by applying particle swarm optimization (PSO) algorithm a sliding window for better prediction of spacecraft telemetry data (denoted as PGMSW model). In order to produce more accurate predictions, background-value optimization is specially carried out using the particle swarm optimization technique in conventional GM (1, 1). For verifying PGMSW, it is utilized in the prediction of the cyclic fluctuation of telemetry series data and exponential variations therein. The simulation results indicate that the PGMSW model provides accurate solutions for prediction problems similar to the pseudo-periodic telemetry series.

show abstract

Section: Grey-markov Modelmentioning

confidence: 99%