Comparison of Adaptive Neuro-fuzzy and Particle Swarm Optimization based Neural Network Models for Financial Time Series Prediction
Bibliographic record
Abstract
Artificial neural networks (ANNs) can be a potential tool for non-linear processes that have unknown relationship and as a result are difficult to fit (Darbellay & Slama 2000). ANNs are non-linear, data-driven and self adaptive approaches as opposed to the above model-based non-linear methods. One of the major application areas of ANNs is forecasting (Zhang, Patuwo, & Hu, 1998). ANN can identify and learn correlated patterns between input data sets and corresponding target values. This technique is In this paper, an attempt has been made to assess the forecasting ability of adaptive neuro-fuzzy inference system (ANFIS) with the traditional feed forward neural network using financial time series data. Also, efforts have been made to examine the performance of particle swarm optimization algorithm for training neural networks. This algorithm is shown to perform well in the current study.
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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 | 0.002 | 0.004 |
| 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".