Research on SARIMA-LSTM Crime Prediction Model Based on Nonlinear Combination of RBF Neural Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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