SARIMA-GRU Crime Prediction Model Based on Nonlinear Combination of BP Neural Network
Bibliographic record
Abstract
Aiming at the problems that the current crime prediction model can not capture the composite characteristics of crime sequence data or respond to the dynamic changes of the environment in time, a SARIMA-GRU crime prediction model based on nonlinear combination of BP neural network is proposed. This model nonlinearly combines the prediction results of SARIMA and GRU models on the number of crimes through BP neural network, uses the back-propagation algorithm to learn the weight, and takes the weight matrix determined by each layer of neurons as the weight of the two methods in the combined model. Comprehensive utilization is taken of the advantages of SARIMA model in linear time series prediction and GRU in nonlinear feature mining, so as to obtain better crime prediction results. Through the open crime data of Vancouver and San Francisco to compare the combined model with other models, the experimental results show that the combined prediction model proposed in this paper can capture the composite characteristics of crime time series data, and has higher accuracy than other crime prediction models.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".