Time-Series Forecasting With Disentangled Trend and Seasonal Features
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Solution Overview
Problem
Existing time-series forecasting systems face challenges in effectively utilizing unlabeled data due to entangled seasonal and trend representations, leading to model over-fitting and poor generalization in nonstationary environments, particularly in high-dimensional and complex data scenarios like medical ECG and EEG signals.
Innovation Solution
A framework for learning disentangled seasonal and trend representations using contrastive learning, employing a backbone encoder, trend feature disentangler, and seasonal feature disentangler, with time and frequency domain contrastive losses to decouple seasonal and trend components, enabling robust forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If supervised end-to-end learning is used for time-series forecasting, then forecasting performance can be achieved, but the system cannot effectively utilize unlabeled data and requires expensive manual annotation
Solution Approach 1:
The system performs self-supervised learning where the model creates its own training data by learning temporal patterns and relationships from unlabeled time-series data. The autoencoder architecture enables the system to reconstruct input sequences and predict future values without requiring external labels, allowing the model to train itself using abundant unlabeled data from sources like ECG and EEG signals
Solution Approach 2:
The system creates synthetic training data by copying and transforming the input time-series data through the autoencoder architecture. The encoder-decoder structure generates reconstructed sequences and predicted future values that serve as training examples, effectively replicating the function of labeled data without requiring actual human annotations
2Reliability
If deep learning models are trained end-to-end on time-series data, then forecasting can be performed, but the models over-fit to noise and fail to generalize in nonstationary environments
Solution Approach 1:
The system segments the time-series data into distinct temporal patterns and relationships through the autoencoder architecture. The encoder learns to represent underlying patterns while the decoder reconstructs sequences and predicts future values. This segmentation separates signal from noise by learning invariant temporal relationships that generalize across nonstationary environments
Solution Approach 2:
The system implements feedback mechanisms where the predicted future values and reconstructed sequences are used to refine the model's understanding of temporal patterns. The loss functions compare predicted and actual values, continuously adjusting the model parameters to improve generalization capability while reducing overfitting to noise in the training data
Data Source
AI summary
A method includes receiving, via a data interface, a training dataset of time-series data samples; and generating, by an encoder of a representation training model, intermediate representations of a training data sample from the training dataset. One or more trend feature representations are generated based on the intermediate representations. One or more seasonal feature representations are generated based on the intermediate representations. The representation training model is trained, using the one or more trend feature representations and one or more seasonal feature representations, to generate a trained representation training model.