System Analysis and Forecast of Yield Time Series Based on Neural Network Technologies
Bibliographic record
Abstract
With the help of neural networks, it is possible to automate the processes of pattern recognition, adaptive control, forecasting, creating expert systems, etc. Neural networks can successfully solve problems that traditional methods cannot cope with, relying on incomplete, noisy or modified information.In its pure form, neural network modelling is based solely on data without using a priori theories.The North Kazakhstan region is the object of the study.The paper uses the theory of random processes and high-order Markov chains, allowing to build a model of a series; statistics and econometrics, which are used to select a plurality of lagged variables that affect predictive indicators, and to estimate the probability matrices of transitions between states; nonlinear optimisation methods to construct a nonlinear trend model with harmonic components aimed at predicting the low-frequency component of a time series.The artificial neural network will be implemented using Matlab.Studies have shown that the yield time series are persistent, that is, they have the effect of "long-term memory".Autoregressive models cannot act as an adequate tool for modelling and forecasting such series.The practical significance of the study lies in the possibility to use the results to enhance plant yield at agricultural enterprises.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".