Multi-Horizon Time Series Forecasting with Attention and LSTM
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current neural network architectures for time-series forecasting often fail to accurately capture both short-term and long-term temporal characteristics, leading to inadequate multi-horizon forecasting, especially when dealing with heterogeneous data that includes time-dependent and time-independent variables.
Innovation Solution
A system incorporating a sequence-to-sequence layer and a temporal self-attention layer processes both short-term and long-term temporal characteristics, using a variable selection layer to assign weights to input variables and a gated residual network to enrich static covariates, enabling the system to generate accurate and interpretable forecasts across multiple horizons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single neural network architecture is used for time-series forecasting, then the model structure is simple, but it fails to accurately capture both short-term and long-term temporal characteristics
Solution Approach 1:
The patent divides the forecasting task into multiple horizons (short-term, medium-term, long-term) and uses separate neural network components (encoder-decoder LSTM for short-term, self-attention mechanism for long-term) to handle different temporal characteristics. This segmentation allows each component to specialize in capturing specific temporal patterns without being overwhelmed by the full complexity of multi-horizon forecasting.
Solution Approach 2:
The patent merges multiple neural network architectures (encoder-decoder LSTM and self-attention mechanism) into a unified multi-horizon forecasting system. The encoder shares representations between the two decoding paths, combining the sequential processing strength of LSTMs with the global context capture capability of self-attention, thereby achieving both short-term and long-term forecasting accuracy.
2Measurement precision
If all input variables are processed equally, then the processing is simple, but the system cannot handle heterogeneous data with varying importance
Solution Approach 1:
The patent applies different processing strategies to different input variables based on their characteristics. The variable selection layer assigns different weights to different input variables, allowing the system to treat important variables differently from less important ones. This local differentiation in processing quality enables the system to handle heterogeneous data effectively by focusing computational resources on the most relevant features.
Solution Approach 2:
The patent dynamically changes the parameters (weights) of input variables through the variable selection layer, which learns the relative importance of each variable. This parameter adjustment allows the system to adapt to heterogeneous data by automatically assigning higher weights to more informative variables and lower weights to less relevant ones, thereby improving forecasting accuracy without manual feature engineering.
3Measurement precision
If the model processes all time-series data in detail, then comprehensive information is captured, but overfitting occurs and explainability decreases
Solution Approach 1:
The patent extracts and processes only the most relevant temporal characteristics from the input data through the variable selection layer and attention mechanisms. Instead of processing all time-series data in equal detail, the system identifies and focuses on key patterns and features that are most predictive of future values, thereby capturing comprehensive information where needed while avoiding overfitting on noisy or less relevant data points.
Solution Approach 2:
The patent incorporates attention mechanisms that provide feedback about the importance of different time steps and variables to the forecasting process. The attention weights act as a form of feedback that highlights which parts of the input data are most relevant for each forecasting horizon, improving both the accuracy and interpretability of the model by making the decision-making process transparent and adaptive.
Data Source
AI summary
Methods, systems, and apparatus, including computer storage media, for performing multi-horizon forecasting on time-series data. A method includes determining short-term temporal characteristics for respective forecasting horizons of one or more time-steps. The determining can include generating, using RNN encoders, encoder vectors based on static covariates, and time-varying input data; and predicting using one or more RNN decoders, a short-term pattern for a respective future time period. The method can also include capturing long-term temporal characteristics for the respective forecasting horizons based on the static covariates, the time-varying input data captured during the respective past time-periods, and the time-varying known future input data.