Convolution‐based filtering and forecasting: An application to WTI crude oil prices
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We introduce new methods of filtering and forecasting for the causal–noncausal convolution model. This model represents the dynamics of stationary processes with local explosions, such as spikes and bubbles, which characterize the time series of commodity prices, cryptocurrency exchange rates, and other financial and macroeconomic variables. The convolution model is a structural mixture of independent latent causal and noncausal component series. We propose an algorithm that recovers the latent components by evaluating the filtering density of one component, conditional on the observed past, present, and future values of the time series. Forecasts of the observed time series are obtained as a combination of filtered causal and noncausal component forecasts. The new filtering and forecasting methods are illustrated in a simulation study and compared with the results obtained from the mixed causal–noncausal autoregressive MAR model in application to WTI crude oil prices.
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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 | 0.001 | 0.001 |
| 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