Using time series methods to predict the value of agricultural output and some financial indicators affecting it in Iraq for the period (2021q1-2025q4)
Bibliographic record
Abstract
The aim of the research is to predict the value of agricultural output and some fiscal policy variables using quarterly data from the first quarter of 2021 until the fourth quarter of 2025, through the application of different time series methods (random behavior, general trend, moving averages, simple exponential smoothing, Brown’s method In the exponential smoothing, ARIMA models) on each of the following variables (value of agricultural output, oil prices, government spending, GDP, agricultural investment, agricultural imports), and the results showed that ARIMA (1,0,1) model is the best A model for forecasting oil prices until the fourth quarter of 2025, and the results indicated that the general trend model is the best model for predicting the government spending variable until the fourth quarter of 2025, while the ARIMA (1,1,1) model was the model chosen to predict the variable GDP until the fourth quarter of 2025, as well as it became clear from the results that the best model used for prediction agricultural investment is the exponential smoothing model, while the ARIMA (2,0,4) model was the best model for forecasting agricultural imports until the fourth quarter of In 2025, the results also indicated that the best model that can be employed to predict the variable value of agricultural output is the quadratic trend model according to the predictive ability tests of different models..
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".