Prediction of Tourist Flow Based on Deep Belief Network and Echo State Network
Bibliographic record
Abstract
The accuracy of tourist flow prediction is crucial to the sustainable development of tourism industry. However, it is very difficult to forecast the highly nonlinear tourist flow in an accurate manner. The artificial neural network (ANN) has been widely adopted to predict nonlinear time series, but its shallow structure cannot effectively learn the features of high-dimensional tourist flow data. To solve the problem, this paper puts forward a tourist flow prediction model based on deep learning (DL). First, the deep belief network (DBN), with its strong ability to extract nonlinear features, was employed to extract effective features through unsupervised learning of historical tourist flow. Next, the echo state network (ESN) was effectively fused with the DBN. The ESN was placed at the top layer of the tourist flow prediction model, serving as the logic regression layer. Finally, the offline training and prediction effects of the proposed ESN-DBN were verified through experiments on the holiday tourist flow data extracted from a tourist center, and compared with those of two classical prediction models, namely, backpropagation neural network (BPNN) and autoregressive integrated moving average (ARIMA). The results show that the ESN-DBN achieved a mean absolute percentage error was below 12% and a rational computing time; the proposed model also outperformed the two classical models in prediction accuracy. The research results provide an important reference for the forecast of tourist flow and planning of tourism development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".