Online Data Forecasting via Time Series Decomposition
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Solution Overview
Problem
Conventional data forecasting methods are resource-intensive and inefficient, particularly when using batch algorithms, which can lead to inaccurate predictions due to stale models and long processing times, especially in real-time data processing environments.
Innovation Solution
The implementation of online data forecasting that decomposes time series data into trend, seasonality, and residual components in real-time, allowing for accurate and efficient forecasting of subsequent data points as they arrive, using methods like Seasonal and Trend decomposition using LOESS (STL), enabling immediate analysis and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If batch algorithms are used for data forecasting, then resource consumption is high, but processing time is long and prediction accuracy deteriorates due to stale models
Solution Approach 1:
The patent transitions from static batch processing to dynamic online forecasting that continuously adapts as new data arrives. The system maintains and updates forecast models in real-time, allowing the model to dynamically adjust to new patterns without requiring complete reprocessing of historical data.
Solution Approach 2:
The system performs preliminary decomposition of time series data into trend, seasonality, and residual components once, then uses these pre-decomposed components for rapid forecasting. This preliminary action avoids repeated decomposition during each forecasting operation, significantly reducing processing time while maintaining accuracy.
2Productivity
If batch processing is used, then resource efficiency is low, but the system can handle large volumes of historical data
Solution Approach 1:
The patent extracts and stores the decomposed components (trend, seasonality, residual) separately from the raw data. This extraction allows the system to work with compact representations rather than full historical datasets, reducing memory usage and computational resources required for forecasting operations.
Solution Approach 2:
The system changes the representation parameters of the data by transforming raw time series into decomposed components with specific mathematical properties. This parameter transformation enables more efficient computation and storage, as the decomposed form requires fewer resources to process while preserving the essential patterns needed for forecasting.
3Measurement precision
If online forecasting is implemented, then real-time prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the forecasting problem into distinct components: trend estimation, seasonality modeling, and residual analysis. Each component is processed separately using specialized algorithms optimized for that specific aspect, reducing the overall computational complexity compared to applying a single complex model to the entire time series.
Data Source
AI summary
Embodiments of the present disclosure are directed to facilitating performing online data forecasting. In operation, data decomposition of an incoming data point is performed to determine a trend component associated with the incoming data point. Such a trend component, and previous trend components, can be used to determine a trend component expected for a data point subsequent to the incoming data point. A seasonality component expected for the data point subsequent to the incoming data point can be identified, for example, based on a seasonality component associated with a previous corresponding data point. Thereafter, the expected trend and seasonality components can be used to predict the data point subsequent to the incoming data point. Such a data prediction can be performed in an online processing manner such that a subsequent data point is not used to decompose the incoming data point or forecast the data point.