Dynamic Forecasting Model Selection Balancing Accuracy and Cost
Solution Overview
Problem
Existing machine learning systems face challenges in accurately forecasting cashflows and product flows due to high computational resource requirements, often exceeding available resources in businesses or entities.
Innovation Solution
A method for dynamically selecting and applying forecasting models for time series data streams by estimating accuracy and cost metrics, and solving an operations research resource allocation model to balance forecasting accuracy and computational cost constraints, using integer programming or genetic algorithms to optimize model selection and training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning systems use accurate forecasting models for time series data, then forecast accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The system dynamically selects forecasting models based on current constraints and data characteristics. The model selection is not static but adapts over time, choosing different models (e.g., ARIMA, exponential smoothing, machine learning models) depending on the specific time series properties and available computational resources at each forecasting step.
Solution Approach 2:
The system changes parameters such as model complexity, training data size, and forecasting horizon based on computational constraints. When computational resources are limited, the system adjusts by using simpler models or reducing the amount of historical data used for training, thereby maintaining forecast accuracy within acceptable bounds while reducing computational cost.
2Measurement precision
If multiple forecasting models are trained and applied to each data stream, then forecast accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the forecasting task by dividing time series data into different categories or groups based on their characteristics (e.g., trend strength, seasonality, noise level). Different forecasting models are then assigned to different segments, so that simple models handle simple patterns and complex models handle complex patterns, reducing overall system complexity while maintaining accuracy.
Solution Approach 2:
The system employs a universal model selection framework that can handle multiple types of time series data with different characteristics using a single unified approach. The same model selection algorithm and evaluation metrics are applied across all data streams, providing a multi-functional solution that reduces complexity compared to having separate specialized systems for each data type.
3Measurement precision
If computational resources are allocated to train and run multiple forecasting models, then forecast accuracy is improved, but productivity of resource allocation decreases
Solution Approach 1:
The system applies partial action by not training or evaluating all possible forecasting models for every data stream. Instead, it selectively applies only the necessary subset of models based on data characteristics and constraints. This partial action approach maintains forecast accuracy by using appropriate models where needed while improving resource allocation efficiency by avoiding unnecessary model training and evaluation.
Solution Approach 2:
The system performs preliminary analysis of time series data characteristics before selecting and training forecasting models. By pre-processing the data to identify patterns, trends, and appropriate model types, the system avoids wasting computational resources on unsuitable models. This preliminary action improves both forecast accuracy (by selecting appropriate models) and resource allocation efficiency (by avoiding unnecessary computations).
Data Source
AI summary
Systems and methods for forecasting future values of data streams are disclosed. One example method may include receiving information characterizing each of a plurality of forecasting models, retrieving historical data for each of a plurality of data streams, determining one or more constraints, dynamically selecting one of the plurality of forecasting models for each of the data streams based on accuracy metrics for the forecasting models, estimating cost metrics associated with each forecasting model, dynamically selecting the forecasting model based at least in part on the accuracy metrics, the cost metrics, and the determined constraints, and forecasting a first subsequent value of each data stream using the corresponding selected forecasting model.