Neural Network Time Series Forecasting via Natural Language Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing time series forecasting methods using numerical prediction models are inflexible and struggle to incorporate contextual data effectively, leading to inaccurate forecasts and increased complexity when adapting to new types of contextual information.
Innovation Solution
A natural language processing (NLP) model is employed to forecast time series by converting historical data into natural language, allowing for easy integration of contextual data and using a neural network with a natural language processing branch and a numerical time-series prediction branch connected by a combined loss function to generate accurate forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional numerical prediction models are used for time series forecasting, then the model structure is relatively simple, but the model is inflexible and struggles to incorporate contextual data effectively
Solution Approach 1:
The model is divided into two distinct branches: a natural language processing branch that handles contextual information and a numerical time-series prediction branch that handles quantitative forecasting. This segmentation allows each branch to specialize in its respective data type while maintaining overall model flexibility and manageability.
Solution Approach 2:
The natural language processing branch is designed to handle multiple types of contextual data (text descriptions, categorical information, external factors) through a unified language template approach. This universal interface enables the model to incorporate diverse contextual information without requiring separate processing pathways for each data type.
2Measurement precision
If traditional numerical prediction models are used, then the model structure is simpler, but forecasting accuracy decreases when contextual data needs to be incorporated
Solution Approach 1:
The model merges the natural language processing branch and the numerical prediction branch through a combined loss function that jointly optimizes both language generation accuracy and time-series prediction precision. This merging ensures that contextual understanding and numerical forecasting work together synergistically to improve overall forecasting accuracy.
Solution Approach 2:
The natural language processing branch acts as an intermediary that transforms diverse contextual data into structured language representations that the numerical prediction branch can effectively utilize. This intermediary layer bridges the gap between unstructured contextual information and structured numerical forecasting requirements.
3Adaptability or versatility
If numerical prediction models are used, then implementation is simpler, but adapting to new types of contextual information increases complexity
Solution Approach 1:
The model uses language templates with parameterized structures that can be easily adjusted to accommodate new types of contextual information. By changing the template parameters and language formulations rather than the underlying model architecture, the system adapts to new data types while maintaining implementation simplicity.
Data Source
AI summary
Methods, systems, devices, and neural networks are for forecasting a time series. According to one example, a natural language processing engine receives input natural language describing historical data of a time series and, based on the input natural language, generates output natural language which describes a forecast for the time series.