Box Office Prediction Model Incremental Update
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Solution Overview
Problem
Current methods for predicting film box office trends are limited in accuracy and usefulness due to their reliance on simple models that only predict daily or weekly box office figures, lacking the ability to provide long-term forecasts and being constrained by training data limitations.
Innovation Solution
A method and device that acquire real-time dynamic factor data to incrementally update a pre-trained box office prediction model, allowing for continuous refinement and improved accuracy in predicting film box office trends over preset periods, using a combination of data acquisition, normalization, and model retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a simple linear regression model or classification model is used to predict box office, then the model is easy to implement and train quickly, but the prediction accuracy is limited and cannot provide long-term forecasts
Solution Approach 1:
The patent transforms the prediction approach by changing from static model parameters to dynamic time-series parameters. It introduces temporal dimensions (daily, weekly, monthly predictions) and updates model parameters continuously based on accumulating data, thereby improving prediction accuracy while maintaining implementation feasibility through systematic parameter evolution
Solution Approach 2:
The patent applies dynamics by making the prediction model adaptive over time. Instead of using a fixed model, it continuously updates predictions based on new data arrivals, allowing the model to adapt to changing market conditions and improve accuracy for both short-term and long-term forecasts
2Productivity
If daily or weekly box office prediction is made at a fixed time, then the prediction process is simple and quick, but the prediction value is limited for operational decisions and accuracy is affected by training data restrictions
Solution Approach 1:
The patent segments the prediction process into multiple time granularities (daily, weekly, monthly predictions) and multiple decision stages. This allows different prediction horizons to serve different operational needs, improving reliability for various decision-making scenarios while maintaining quick prediction capabilities through modular processing
Solution Approach 2:
The patent implements feedback mechanisms where prediction results and actual box office data are continuously compared and used to refine future predictions. This feedback loop enhances prediction reliability by learning from past performance and adjusting models accordingly, while maintaining operational efficiency through automated feedback processing
Data Source
AI summary
Embodiments of the present disclosure provide a method and a device for predicting a box office trend of a film, a device and a storage medium. The method includes acquiring in real time a plurality of dynamic factor data of each of various films to be shown, in which, the dynamic factor data represents a factor that influences box office of the film; after a film in the various films is shown, incrementally updating a pre-trained box office prediction model by using box office data and the plurality of dynamic factor data of the film; and according to a preset period, predicting a box office trend of a target film to be predicted in the various films by using a box office prediction model incrementally updated in each preset period and the plurality of dynamic factor data of the target film, to obtain a plurality of prediction results.