Research on SARIMA-LSTM Crime Prediction Model Based on Nonlinear Combination of RBF Neural Network
Bibliographic record
Abstract
Addressing the limitations of existing crime prediction models in capturing the composite features of crime time series data and responding promptly to environmental changes, this paper designs a crime prediction model based on the non-linear combination of Radial Basis Function (RBF) neural network, SARIMA, and LSTM. In this model, the prediction results of crime quantities from SARIMA and LSTM undergo non-linear combination through an RBF neural network, utilizing backpropagation algorithm for weight learning. The weight matrices determined by each layer’s neurons function as the proportions of the two methods in the combined prediction. By synergizing the advantages of the SARIMA model in linear time series prediction and the LSTM network in non-linear feature exploration, the model aims to enhance predictive accuracy. Experimental comparisons with real crime data from Vancouver and San Francisco affirm that the SARIMA-LSTM model, grounded in the non-linear combination of RBF neural network, excels in capturing the composite features of crime time series data, exhibiting superior accuracy compared to other models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".