Token-Based Adaptive Time-Series Prediction by Ensembling Linear and Non-linear Estimators: A Machine Learning Approach for Predictive Analytics on big Stock Data
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- Metaresearch, Meta-epidemiology (narrow)
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Simulation or modelingConsensus signal: none
- Genre
- Candidate signal: MethodsConsensus signal: Methods
- Teacher disagreement score
- 0.719
- Threshold uncertainty score
- 1.000
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.212 · how far apart the two teachers sit on this one work
- Validation status
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
Abstract
With technological advancements, big data can be easily generated and collected in many applications. Embedded in these big data are useful information and knowledge that can be discovered by machine learning and data mining models, techniques or algorithms. A rich source of big data is stock exchange. The ability to effectively predict future stock prices improves the economic growth and development of a country. Traditional linear approaches for prediction (e.g., Kalman filters) may not be practical in handling big data like stock prices due to highly nonlinear and chaotic nature. This lead to the exploitation of various nonlinear estimators such as the extended Kalman filters, expert systems, and various neural network architectures. Moreover, to lessen the potential shortcomings of individual algorithms, ensemble approaches have been created by averaging values across different algorithms. Existing ensemble techniques mostly basket-together a collection of sample-based algorithms that are catered to nonlinear functions. To the best of our knowledge, traditional linear estimators have not yet been incorporated into such an ensemble. Hence, in this paper, we propose a machine learning (specifically, token-based ensemble) algorithm that utilizes both linear and nonlinear estimators to predict big financial time-series data. Our ensemble consists of a traditional Kalman filter, long short-term memory (LSTM) network, and the traditional linear regression model. We also explore the adaptive properties in short-term high-risk trading in the presence of noisy data like stock prices and demonstrate the performance of our ensemble.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Topic
- Stock Market Forecasting Methods
- Field
- Decision Sciences
- Canadian institutions
- University of Manitoba
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Keywords
- Computer scienceBig dataKalman filterEstimatorTime seriesMachine learningArtificial neural networkData miningArtificial intelligenceEnsemble learningPredictive analyticsAlgorithmMathematicsStatistics
- Has abstract in OpenAlex
- yes