Multi-Channel Demand Prediction Using Region-Specific Machine Learning Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Predicting demand for products and services in geographic regions is complex due to varying influences from density, weather, events, and temporal factors, with different variables affecting regions differently.
Innovation Solution
An analytics platform using machine learning networks with multiple demand prediction models, including anomaly detection and time series forecasting, trained on channel-specific features to generate accurate demand metrics for real-time and future conditions, enabling surge pricing and inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single demand prediction model is used, then the system complexity is low, but the prediction accuracy is insufficient due to the diverse and region-specific variables affecting demand
Solution Approach 1:
The patent divides the demand prediction system into multiple specialized models, each trained on specific channel features and tailored to particular geographic regions or market segments. This segmentation allows each model to focus on region-specific variables (e.g., weather patterns for coastal regions, event density for urban areas) thereby improving prediction accuracy without requiring a single overly complex universal model
Solution Approach 2:
The system implements local quality by creating channel-specific prediction models that are trained on locally relevant features for each geographic region. Each model incorporates region-specific variables such as local weather conditions, cultural events, and demographic characteristics, enabling accurate predictions that account for the unique demand drivers of each location rather than using a one-size-fits-all approach
2Measurement precision
If multiple channel-specific features are collected and processed, then the demand prediction accuracy improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the data processing workflow by organizing features into distinct channels (e.g., weather data, event data, demographic data) and assigning specific prediction models to process each channel's features independently. This segmentation allows for targeted processing of relevant features for each region, improving accuracy while managing computational complexity through selective processing rather than processing all available data uniformly
Solution Approach 2:
The system applies local quality to data processing by selecting and processing only the features most relevant to each specific geographic channel. Each prediction model processes a customized subset of features tailored to its target region, such as processing weather data for regions where weather significantly impacts demand, while ignoring irrelevant features from other regions, thereby reducing overall data processing complexity
Data Source
AI summary
This disclosure relates to artificial intelligence (AI) and machine learning networks for predicting or determining demand metrics across multiple channels. An analytics platform can receive channel events from multiple channels corresponding to geographic areas, and channel features related to demand conditions in the channels can be extracted from the channel events. During a training phase, the channel features can be accumulated into one or more training datasets for training one or more demand prediction models. The one or more demand prediction models can be trained to predict or determine demand metrics for each of the channels. The demand metrics can indicate or predict demand conditions based on the current conditions in the channels and/or based on future, predicted conditions in the channels. Other embodiments are disclosed herein as well.