Forecasting Sparse Data Streams via Neural Network Clustering
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Solution Overview
Problem
Traditional time series forecasting approaches struggle with accuracy and speed when dealing with sparse data streams, leading to inefficiencies in processing time, memory management, and increased power consumption due to the limited number of data points available for forecasting.
Innovation Solution
A method and system that utilize neural networks to generate forecast features, transform them using metric learning, cluster the features, and initialize a forecast learning algorithm with combined weights from clusters, enabling faster and more accurate forecasting in sparse data streams by leveraging dense data streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time series forecasting approaches are used on sparse data streams, then forecasting can be performed, but forecasting accuracy deteriorates due to limited data points
Solution Approach 1:
The patent combines multiple sparse data streams into a unified forecasting model, merging information from different sources to compensate for individual data scarcity. This allows the system to achieve accurate forecasts even when individual data streams contain limited data points.
Solution Approach 2:
The patent introduces an intermediary representation layer that transforms sparse input data into a denser feature space. This intermediary representation captures underlying patterns and relationships, enabling accurate forecasting without directly relying on the limited raw data points.
2Speed
If conventional forecasting methods are used, then forecasting can be performed, but processing speed deteriorates leading to slower computation
Solution Approach 1:
The patent performs preliminary actions by pre-processing and transforming data into an optimized representation format before actual forecasting. This includes creating intermediate feature representations and pre-computing certain statistical measures, which significantly accelerates the subsequent forecasting computation.
Solution Approach 2:
The patent replaces conventional mechanical forecasting approaches with a neural network-based system that leverages parallel computation capabilities. This substitution enables faster processing by utilizing distributed computational architectures rather than sequential mathematical operations.
3Use of energy by moving object
If conventional forecasting is performed with computing systems, then forecasting can be achieved, but power consumption increases due to slower processing
Solution Approach 1:
The patent changes key parameters of the forecasting system, including data representation format, model architecture, and computation optimization settings. These parameter changes enable the system to achieve the same forecasting accuracy with significantly reduced computational overhead and lower power consumption.
Data Source
AI summary
Methods and systems for forecasting in sparse data streams. In an example embodiment, steps or operations can be implemented for mapping a time series data stream to generate forecast features using a neural network, transforming the forecast features into a space with transformed forecast features thereof using metric learning, clustering the transformed forecast features in a cluster, initializing a forecast learning algorithm with a combination of the transformed forecast features in the cluster corresponding to a sparse data stream, and displaying forecasts in a GUI dashboard with information indicative of how the forecasts were achieved, wherein the mapping, the transforming, the clustering, and the initializing together lead to increases in a speed of the forecasting and computer processing thereof.