A Novel Hybrid Approach for Daily Tourism Arrival Forecasting: The PROPHET-Bayesian Gaussian Process-Forward Neural Network Model
Bibliographic record
Abstract
In light of the profound impact of the COVID-19 pandemic on the tourism sector, accurate forecasting of daily visitor arrivals has become paramount.Introduced herein is a novel PROPHET-Bayesian Gaussian Process-Forward Neural Network (PROPHET-BGP-FNN) model, an advanced deep learning (DL) approach, devised for this purpose.This model uniquely integrates the PROPHET model with a deep neural network, merging BGP and FNN, thereby enabling the detection of both linear and nonlinear data attributes.Linear characteristics are discerned by the PROPHET component.Contrary to traditional methodologies which predominantly employ monthly or quarterly datasets, this approach harnesses the precision of daily data, thereby offering a timely and refined forecast.Given the complexity of daily tourist demand data, which manifests a blend of linear and nonlinear patterns, the conventional frameworks often fall short in representation.Through an application on Hawaiian tourism data spanning 2017 to 2021, 80% of which was employed for training and the remainder for validation, it was observed that the PROPHET-BGP-FNN model surpassed benchmark models, including Long Short-Term Memory (LSTM)-SARIMAX-PROPHET, with a remarkable forecast accuracy of 97%.This investigation underscores the viability of integrating high-frequency data with cutting-edge machine learning (ML) methodologies for a more precise forecast in tourism demand.Such insights hold significant implications for strategic decision-making, thereby enhancing the tourism sector's economic viability and competitive stance.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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 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".