Multi-Task Deep Learning Model for Time Series Imputation and Forecasting
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Solution Overview
Problem
Multivariate time series forecasting with missing values is challenging due to the separation of imputation and forecasting tasks, leading to inaccurate predictions and resource inefficiencies, as existing methods perform these tasks sequentially rather than simultaneously and lack access to original data.
Innovation Solution
A multi-task time series model that simultaneously performs imputation and forecasting tasks using a deep learning approach, training two functions to determine relationships between features and predict missing values and future data, allowing for flexible handling of full and missing data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If imputation and forecasting tasks are performed sequentially using existing methods, then the system can handle missing values, but the prediction accuracy deteriorates and computational resources are wasted
Solution Approach 1:
The patent combines the imputation task and forecasting task into a single unified deep learning model that processes both tasks simultaneously. The model takes multivariate time series data with missing values as input and outputs both imputed values for missing timestamps and forecasted values for future timestamps in one computational pass, eliminating the need for sequential processing and reducing redundant computations.
Solution Approach 2:
The deep learning model is designed with multi-functionality to handle both imputation and forecasting tasks within a single system. The model architecture includes shared layers that learn general temporal patterns from the input data, which are then utilized for both filling missing values and predicting future values, making the system universally applicable to both tasks without requiring separate specialized models.
2Measurement precision
If existing methods separate imputation and forecasting tasks, then the system structure remains simple, but the model cannot access original data for both tasks leading to inaccurate predictions
Solution Approach 1:
The unified model is segmented into distinct functional components: an input layer that receives multivariate time series data, shared processing layers that learn temporal patterns, an imputation output layer that generates filled values for missing timestamps, and a forecasting output layer that generates predictions for future timestamps. This segmentation allows the model to access and process original data for both tasks simultaneously while maintaining a clear structural organization.
3Loss of energy
If sequential imputation and forecasting is performed, then computational resources are wasted, but the processing steps remain manageable
Solution Approach 1:
The deep learning model implements continuous useful action by processing imputation and forecasting tasks in a single uninterrupted computational forward pass. The model continuously processes the input time series data through shared layers, generating both imputed and forecasted outputs simultaneously without pausing or reprocessing, thereby maximizing computational efficiency and eliminating wasted resources.
Data Source
AI summary
A prediction system may identify a first set of features of training data and a second set of features of the training data. The prediction system may train a deep learning model using the training data. Training the deep learning model may comprise training a first function to determine a relationship between the first set of features and the second set of features. Training the deep learning model may further comprise training a second function to determine a relationship between missing data of a first period of time and complete data of a second period of time that follows the first period of time. The prediction system may generate imputation time series data and forecasted time series data using the trained deep learning model. The imputation time series data is generated based on an imputation task and the forecasted time series data is generated based on a forecasting task.