Mixture-of-Experts ETA Forecasting With Time-Series Uncertainty
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Solution Overview
Problem
Existing arrival time forecasting systems produce inaccurate forecasts and fail to communicate the inherent uncertainty in estimated time of arrival predictions, particularly in large-scale item fulfillment services, due to challenges in capturing complex patterns and variability in data.
Innovation Solution
A machine learning model utilizing a Mixture of Experts (MoE) architecture with specialized encoders for categorical, continuous, and time-series data, combined with multitask learning and probabilistic modeling, to improve accuracy and quantify uncertainty in arrival time forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional tree-based models are used for arrival time forecasting, then the system is simple to implement, but the forecast accuracy is insufficient and cannot capture complex patterns in data
Solution Approach 1:
The model segments the forecasting task into multiple specialized encoder sub-models (expert encoders), each designed to capture specific patterns in the data. These encoders process different aspects of the input features independently before combining their outputs, allowing the system to handle complex patterns while maintaining modularity and manageability.
Solution Approach 2:
The forecasting system uses a composite architecture combining multiple types of encoders (categorical encoder, continuous encoder, time series encoder) that work together to process different feature types. This composite structure enables the model to capture diverse patterns in the data that single-model approaches cannot handle effectively.
2Measurement precision
If multiple features and time series data are incorporated to capture complex patterns, then forecast accuracy improves, but the computational complexity and processing requirements increase
Solution Approach 1:
By dividing the processing into separate encoder modules, each handling specific feature types, the computational workload is distributed across multiple specialized components rather than requiring a single monolithic model. This segmentation allows for more efficient resource utilization while processing comprehensive feature sets.
Solution Approach 2:
The encoder architecture is designed to be multi-functional, with each encoder capable of processing its specific feature type efficiently. This universal design allows the system to handle diverse input data (categorical, continuous, time series) without requiring separate processing pipelines for each feature type, optimizing computational resources.
3Loss of information
If the model provides detailed probabilistic forecasts, then uncertainty communication improves, but the model complexity and training difficulty increase
Solution Approach 1:
The probabilistic forecasting capability is implemented as a distinct component within the overall architecture. The model segments the prediction output into probabilistic distributions that communicate uncertainty, separate from the point estimate generation. This modular approach allows uncertainty communication without fundamentally redesigning the entire model architecture.
Data Source
AI summary
Methods, systems, and machine learning models for providing accurate estimate time of arrival (ETA) predictions are disclosed, particularly in the context of item fulfillment services. Input features including continuous, numerical, categorical, and time series features can be processed using an initial set of encoders. The resulting embeddings (and other applicable data) can be applied to a set of “expert” encoders. The embeddings produced by the expert encoders can be combined and processed using a multilayer perceptron, which can return one or more estimated arrival time predictions. Such predictions can correspond to multiple tasks and can include both point estimate predictions and distribution estimate predictions, e.g., predictions describing a probability density function of estimated arrival times. Interval regression can be used to produce distribution estimates, and machine learning models according to embodiments can be trained using multitask learning to produce estimated arrival time predictions for multiple tasks.