Forecasting of commodities prices using a multi‐factor PDE model and Kalman filtering-Reference-Cited by-同舟云学术

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Forecasting of commodities prices using a multi‐factor PDE model and Kalman filtering

Published:2018-11-29 Issue:4 Volume:3 Page:232-245
ISSN:2398-3396
Container-title:IET Cyber-Physical Systems: Theory & Applications
language:en
Short-container-title:IET cyber-phys. syst.

Author:

Rigatos Gerasimos¹,Siano Pierluigi²^ORCID,Ghosh Taniya³,Ding Yi⁴

Affiliation:

1. Unit of Industrial AutomationIndustrial Systems InstituteRion Patras26504Greece

2. Department of Industrial EngineeringUniversity of SalernoFisciano84084Italy

3. IGIDRInstitute of Development ResearchMumbai400065India

4. Department of Electrical EngineeringZhejiang UniversityZhejiang310027People's Republic of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cps.2018.5064

Reference33 articles.

1. State-Space Approaches for Modelling and Control in Financial Engineering

2. Estimating and forecasting the real prices of crude oil: a data‐rich model using a dynamic model averaging approach;Naser H.;Energy Econ.,2016

3. Predicting the oil prices: do technical indicators help?;Yin L.;Energy Convers.,2016

4. Quantifying the economic efficiency impact of inaccurate renewable energy price forecasts;Croonenbrock C.;Energy,2017

5. Extended Kalman filtering for fuzzy modelling and multi‐sensor fusion;Rigatos G.G.;Math. Comput. Model. Dyn. Syst.,2007

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