Neural Time Series Preprocessing for Forecasting
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Solution Overview
Problem
Existing time series forecasting methods face challenges in achieving accurate predictions due to the complexity and unique properties of time series data, such as non-stationarity and self-correlated residuals, particularly because current preprocessing techniques are not integrated with the underlying forecasting models and often require subjective hyperparameter choices.
Innovation Solution
The introduction of neural moving average (Neural MA) and neural locally weighted scatter-plot smoothing (Neural LOWESS) methods, which allow for end-to-end differentiable neural time series preprocessing, enabling learnable weights that process data directly for downstream tasks and are applicable to various neural time series models, transforming non-stationary data and filtering noise effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional preprocessing techniques are used for time series data, then the forecasting model can be simpler, but the preprocessing requires subjective hyperparameter choices and is not integrated with the forecasting model
Solution Approach 1:
The patent merges the preprocessing module with the forecasting model into a unified end-to-end neural network system. The preprocessing operations (moving average, LOWESS smoothing) are implemented as neural network layers that are jointly trained with the forecasting model, eliminating the need for separate preprocessing steps and subjective hyperparameter choices. This integration allows the system to automatically adapt preprocessing parameters based on the specific forecasting task.
Solution Approach 2:
The neural network-based preprocessing system performs self-optimization by learning the optimal preprocessing parameters directly from the data during training. Instead of requiring external hyperparameter tuning, the model automatically adjusts the preprocessing strength and characteristics based on the underlying data patterns and forecasting objectives, making the system self-adaptive to different time series characteristics.
2Measurement precision
If neural network-based preprocessing is integrated with forecasting, then forecast accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the time series processing into distinct functional components within the neural network: an embedding layer for input representation, a preprocessing layer for moving average and LOWESS operations, and a forecasting layer for prediction. This segmentation allows each component to be optimized independently while maintaining end-to-end differentiability, managing computational complexity through modular architecture.
Solution Approach 2:
The system dynamically adjusts preprocessing parameters such as window size for moving average and smoothing factors for LOWESS based on the input data characteristics and training objectives. By making these parameters learnable rather than fixed, the model can adapt the computational complexity to match the specific requirements of different time series datasets, improving forecast accuracy without unnecessary computational overhead.
3Ease of operation
If end-to-end differentiable preprocessing is used, then hyperparameter tuning is reduced, but the model requires learnable weights for preprocessing operations
Solution Approach 1:
The patent replaces the mechanical process of manual hyperparameter tuning with an automated neural network-based learning system. Instead of requiring practitioners to manually select preprocessing parameters, the model uses learnable weights and gradients to automatically optimize preprocessing operations during training. This substitution of manual mechanical tuning with automated neural learning simplifies operation while managing model complexity through standard deep learning techniques.
Data Source
AI summary
Systems and methods for neural time series preprocessing and forecasting, dividing time series data to generate chunks of short time series, inputting each of the short time series to a data preprocessing neural network that includes differencing to transform non-stationary data to stationary data and to filter noise, generating and outputting, from the data preprocessing neural network, processed time series data, and inputting the processed time series data to a forecasting neural network. Parameters of the data preprocessing neural network and parameters of the forecasting neural network are learned end-to-end.