Are Neural Network Models Truly Effective at Forecasting? An Evaluation of Forecast Performance of Traditional Models with Neural Network Model for the Macroeconomic Data of G-7 Countries
Why this work is in the frame
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Bibliographic record
Abstract
Forecasting macroeconomic and financial data are always difficult task to the researchers. Various statisticaland econometrics techniques have been used to forecast these variables more accurately. Furthermore, in the presenceof structural break, linear models are failed to model and forecast. Therefore, this study examines the forecastingperformance of economic variables of G7 countries: France, Italy, Canada, Germany, Japan, United Kingdom andUnited States of America using non-linear autoregressive neural network (ARNN) model, linear auto regressive (AR)and Auto regressive integrated moving average model (ARIMA) models. The economic variables are inflation,exchange rate and Gross Domestic Product (GDP) growth for the period from 1970 to 2015. To measure theperformance of the considered model Root, Mean Square Error, Mean Absolute Error and Mean Absolute PercentageError are used. The results show that the forecasts from the non-linear neural network model are undoubtedly better ascompared to the AR and the Box–Jenkins ARIMA models.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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)
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