3 results about "Autoregressive integrated moving average" patented technology

This disclosure relates generally to method and system for generating key performance indicator prediction model for multi-cloud applications. The disclosed method determines an optimized resource model and a predictive cost structure for one or more multi-cloud applications. The method receives a composite usage request to obtain a current resource consumption metrics and a cost structure for each cloud application identifier (ID). Further, a set of cloud provider API endpoints are invoked to obtain a plurality of usage tracking metrics. Further, a plurality of views are generated for each cloud application ID by processing every record associated with each API response file with allocated resource data. Then, a KPI prediction model is generated by leveraging autoregressive integrated moving average on the KPI time series data to determine an optimized resource model and a cost structure.

The application discloses a high-voltage cable grounding loop resistance prediction method based on data driving, collects historical grounding loop resistance detection records of high-voltage cable sections with different static attribute labels under the same voltage level; adopts iterative threshold variational mode decomposition (ITVMD) to decouple irregular interval multi-loop resistance time series into a plurality of intrinsic mode components with different frequency characteristics; splices frequency domain characteristics of loop resistance mode components with static attributes to construct a comprehensive feature vector, and clusters the comprehensive feature vector through a K-means algorithm; combines a two-way delay embedding transformation (TDT) algorithm, and based on Tucker decomposition, adopts a block Hankel tensor autoregressive integrated moving average (BHT-ARIMA) multi-sequence prediction model to predict the high-voltage cable grounding loop resistance. Compared with the prior art, the application effectively solves the technical problems of low prediction accuracy caused by sparse grounding loop resistance data, complex coupling and missing values, and realizes accurate prediction of the loop resistance.

The application discloses an electromagnetic flowmeter abnormality detection method based on multi-source data analysis, and belongs to the technical field of intelligent detection, which comprises the following steps: synchronously collecting control instruction logs, energy consumption time sequence data, operation parameter data and cross-domain reference data, performing time-space alignment and preprocessing to form an analysis data set; automatically identifying a strategy change node based on an event-response coupling; dividing a strategy section with the strategy change node as a segmentation point, constructing a dynamic energy consumption baseline by using a seasonal decomposition-autoregressive integrated moving average model and adaptively correcting the baseline; executing abnormality diagnosis and root cause determination through a two-stage judgment mechanism, first filtering normal fluctuations caused by strategy adjustment, then accurately distinguishing between two types of abnormalities, namely, poor strategy adaptation and equipment failure, and outputting alarm information and disposal guidance. The application integrates multi-source data and cross-domain reference data, realizes accurate identification of a strategy change node and adaptive iteration of a dynamic baseline, effectively reduces the false alarm rate, and improves the intelligent operation and maintenance level of the electromagnetic flowmeter.