Demand classification based pipeline system for time-series data forecasting

This invention describes a software system designed to forecast future trends in data that changes over time (time series data). It operates by first categorizing the time series into different 'demand classes' based on their unique characteristics. For each class, the system then selects and applies the most suitable forecasting method from a range of options. These operations are organized into a 'pipeline' that runs across multiple computers, enabling efficient processing of large datasets. The claims specify this as a system with a processor and memory, executing instructions, and potentially featuring a graphical user interface to visualize the pipeline.

Why it matters: Since 2019, the maturation of cloud-native distributed computing platforms and automated machine learning (AutoML) for time series has significantly simplified the deployment and optimization of such complex forecasting pipelines. Modern MLOps tools also make managing the lifecycle of these adaptive models much more robust, addressing the heavy software system requirement.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, distributed computing, GUI

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