Genre-Adaptive Time Series Forecasting With Minimal Title Data
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Solution Overview
Problem
Existing time-series methodologies struggle to provide accurate forecasting for content titles with limited historical data, leading to uncertain and inefficient resource allocation in streaming platforms.
Innovation Solution
A method involving genre-adaptive analytic exponential modeling is employed, where training data is dynamically generated based on historical data of similar titles within the same genre, using techniques like polynomial or exponential curve fitting, to enhance forecasting accuracy for both seasonal and non-seasonal content titles with limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Gradient Boosting Machines (GBMs) are used for time-series forecasting, then forecasting accuracy is improved for content titles with sufficient historical data, but forecasting reliability deteriorates for content titles with limited historical data (less than 90 days)
Solution Approach 1:
The patent applies preliminary action by pre-processing historical data to generate derived features (rolling averages, exponential smoothing, trend components) before feeding them to the GBM model. This preliminary data transformation enables the model to learn from limited historical data more effectively, improving both accuracy and reliability for new content titles with less than 90 days of data while maintaining performance for established titles
Solution Approach 2:
The patent segments the forecasting approach by creating separate processing paths for different data scenarios: one path for content titles with sufficient historical data (90+ days) using standard GBM, and another path for titles with limited historical data (<90 days) using enhanced feature engineering and alternative models. This segmentation allows each path to be optimized for its specific condition, resolving the contradiction between accuracy for established titles and reliability for new titles
2Measurement precision
If more historical data is collected for analysis, then forecasting accuracy improves, but time consumption and computational resources increase
Solution Approach 1:
The patent extracts only the most relevant features from historical data rather than processing all available data. By identifying and extracting key patterns (trend, seasonality, recent performance metrics) and feeding only these extracted features to the model, the system achieves high forecasting accuracy while significantly reducing computational time and resource requirements compared to analyzing complete historical datasets
3Measurement precision
If complex machine learning models are used to analyze limited historical data, then forecasting capability improves, but model complexity and computational requirements increase
Solution Approach 1:
The patent applies dynamics by implementing an adaptive model selection mechanism that dynamically chooses between different forecasting approaches based on the characteristics of the input data. For content titles with limited historical data, the system dynamically switches to simplified models with enhanced feature engineering, while for titles with sufficient data, it uses the full GBM capability. This dynamic adaptation improves forecasting capability across different scenarios without requiring all models to be maximally complex
Data Source
AI summary
An improved method is provided to provide efficient and accurate prediction/forecasting of inflow for content titles with limited historical data. The method may include dynamic generation of training data to be supplied to a forecasting model for predicting a performance metric of a content title of interest with limited historical data, based on the limited historical data and/or historical data of one or more other content titles with sufficient history. As such, instead of the limited historical data of the content title, the forecasting model may study from a broader range of historical data that may have similar trends as the title of interest.