Kalman Filter Capacity Forecasting with Time-Series Segmentation
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Solution Overview
Problem
Current methods for forecasting future capacity trends in data storage are inaccurate, leading to excessive equipment purchases and financial waste, as they rely on human observation or threshold alarms that fail to provide reasonable capacity expansion suggestions.
Innovation Solution
A Kalman filter-based method that establishes a dynamical model for capacity time sequences, generates state characteristic signals, segments them to determine segmentation points, and forecasts future capacity using these points, improving forecasting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human observation method is used for capacity forecasting, then professional experience can be utilized to judge trends, but forecasting error remains relatively excessive
Solution Approach 1:
The patent replaces human expert observation with an automated Kalman filter-based forecasting system. The system uses mathematical models and algorithms to process capacity time sequence data, eliminating subjective human judgment while providing more consistent and accurate forecasts. The Kalman filter dynamically adjusts predictions based on historical data patterns, achieving both high accuracy and reliability without relying on human expertise.
2Loss of time
If threshold alarm method is used, then operation and maintenance personnel are reminded in time of purchasing, but reasonable suggestion for purchase amount cannot be provided, leading to excessive capacity expansion and waste
Solution Approach 1:
The patent implements a feedback mechanism where the Kalman filter continuously monitors capacity usage patterns and provides dynamic forecasting. The system analyzes historical capacity data, identifies trends, and predicts future capacity needs with specific quantitative recommendations. This feedback loop enables timely alerts while providing precise purchase amount suggestions, preventing both premature purchases and excessive capacity expansion.
Solution Approach 2:
The system changes the approach from fixed threshold alarms to dynamic parameter-based forecasting. By using Kalman filter to continuously update capacity predictions based on multiple parameters (historical usage, growth rates, seasonal patterns), the system provides adaptive recommendations that adjust to changing conditions, enabling timely and accurate capacity planning without waste.
3Loss of substance
If accurate capacity forecasting is achieved through Kalman filter and segmentation, then purchase cost is reduced and waste is avoided, but system complexity increases
Solution Approach 1:
The patent segments the capacity time sequence data into distinct phases or patterns using the Kalman filter. By dividing the historical data into meaningful segments and analyzing each separately, the system achieves more accurate forecasting while maintaining manageable complexity. The segmentation allows the system to handle different growth patterns independently, reducing the overall computational burden compared to analyzing the entire dataset as a single complex model.
Data Source
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AI summary
Embodiments of the invention provide a Kalman filter based capacity forecasting method, system and computer equipment. The method comprises: acquiring a capacity time sequence of an object to be forecasted; establishing a dynamical model for the capacity time sequence, and extracting a state transition parameter and a process noise parameter of the dynamical model; performing Kalman filter estimation on the capacity time sequence by using the state transition parameter and the process noise parameter to generate at least one state characteristic signal; segmenting the capacity time sequence according to the at least one state characteristic signal, and determining at least one corresponding segmentation point; and forecasting the capacity at future time according to the at least one segmentation point determined in the capacity time sequence. By adopting the technical solutions of the invention, accurate forecasting of the capacity growth is achieved, to facilitate operation and maintenance personnel making a reasonable capacity expansion plan.