Inflation Prophecies : Forecasitng Inflation using Machine Learning Models
Bibliographic record
Abstract
Inflation forecasting is crucial for economic stability and policy-making, yet it remains a challenging task. This study investigates the efficacy of Machine Learning (ML) models, par-ticularly Macro Random Forests (MRF), in forecasting inflation. Comparative analyses are conducted with Long Short-Term Memory (LSTM), Bayesian Vector Autoregressive (BVAR), and Autoregressive Integrated Moving Average (ARIMA) models using Swedish and Canadian data. Results indicate MRF’s has potential for short-term forecasting and elucidates the influence of data size on ML model performance. The study reveals MRF’s adaptability to economic volatility and non-linear relationships, especially during crises like the 2008 recession. Findings underscore both the importance of tailored model selection based on data richness and economic context, and the potential in further investigating Swedish inflation using MRF.
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How this classification was reachedexpand
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".