Business Challenges n the current [mining] economic climate, minimizing costs...
Different Techniques for Predicting Mineral Product Prices
Different Techniques for Predicting Mineral Product Prices
- September 7, 2022
- Posted by: admina
- Category: Uncategorized
Business Challenges
Predicting Mineral Product (MP) prices have been a significant and difficult task usually addressed by econometric, stochastic-Gaussian, and timeseries methods. None of these methods has proved suitable to characterize the dynamic behaviour and time-related nature of MP markets. Chaos Theory (CT) and Machine Learning (ML) methods can signify the temporal relations of variables, and their evolution has been used separately tounderstand better and represent MP markets.
Suggested Solution
Chaos Theorycan determine a system’s dynamics in the form of time delay and embedding dimension. However, this information has often beenexclusively used to define the system’s behaviour and not for predicting. Compared to usual methods, ML has better performance for predicting MP prices due to its capacity for finding patterns governing the system’s dynamics. However, the rational nature of economic complicationsincreases concerns regarding the use of hidden patterns for predicting. Therefore, it is indeterminate if variables are selected, and hidden patter ns found by ML can represent economic rationality. In the face of their refined features representing system dynamics, the different use of either CT or ML does not deliver the expected realistic accuracy. By itself, neither CT nor ML can identify the primary variables affecting systems, recognizethe relation and influence of variables through time, and discover hidden patterns governing systems evolution simultaneously. This projectdiscusses the necessity to adaptand combine CT and ML to obtain a more realistic representation of MP market behaviour to predict long-termprice trends.
Project Info
Start Date
Jun 2021
Duration
24(Months)
Location
Brazil–Australia
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