Comparison of Adaptive Neuro-fuzzy and Particle Swarm Optimization based Neural Network Models for Financial Time Series Prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it