Comparison performance of Artificial Neural Networks and Fuzzy Inference systems in forecasting precious metals price Case Study: Gold, Silver, Platinum and Palladium
Bibliographic record
Abstract
Awareness about the price of precious metals and the correct prediction on the process of taking decision can bring facilities, and purchasing them in the global market and recognizing the specific time of dealing can cause investment. In this article comparison of the performance of Artificial Neural Networks and Fuzzy Inference Systems in predicting the price of the precious metals (Case Study: Gold, Silver, Platinum and Palladium).has been pointed. The information about each of these metals (Gold, Silver, Platinum and Palladium) is monthly considered from 1998 until 2018 including 360 data. Thus, by examining different influential variables, National Product Parameters, Time, getting higher the value of USD dollar against the Canadian dollar, global production and global reserves of precious metals are chosen as influential variables. In this research, implementation of (ANFIS) is made for the prediction model by using Artificial and Fuzzy Neural Model. Evaluation of models by using coefficient values, the average set of squares and the square root of the average set of the squares in order of the values for Gold 0.9964 , 0.000268 & 0.01637 for silver 0.987, 0.000092 & 0.0096, for platinum 0.9976, 0.000209 & 0.01448 and for palladium 0.99, 0.0001 & 0.01 have been achieved. As a result, while the best predictive model for the price of gold and platinum is Artificial Neural Networks, the model of ANFIS is suggested for the silver and palladium.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".