9 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 invention discloses a networking delay dynamic sensing and intelligent compensation method for a distributed test system. According to the method, a technical system of'high-precision perception-intelligent prediction-dynamic compensation-robust guarantee 'is constructed aiming at the networking delay and time variation problem caused by fixed delay, variable delay and system-level errors in a distributed test system; a subnanosecond clock synchronization reference is established through an IEEE 1588 (PTP) precision time protocol; unidirectional delay accurate measurement is realized based on a PTP delay request-response mechanism; modeling and predicting the delay time sequence by adopting an ARIMA (Autoregressive Integrated Moving Average) model, and sending an instruction in advance through dynamic forward compensation so as to achieve a quasi-synchronization test; and the test continuity and the precision controllability when the network state dramatically changes are ensured by combining the self-adaptive model updating and abnormal backspacing mechanism. According to the method, the time sequence synchronization precision and the environmental adaptability of the distributed test system are remarkably improved, and the technical problem that a traditional method is difficult to deal with delay jitter and dynamic change is solved.

The invention discloses a long-term load prediction system and method for a data center, and the system achieves the explicit modeling of trend, seasonal and random components in a load sequence through the optimization and application of a seasonal differential autoregressive moving average model (SARIMA), especially the parameterized description of a long-term seasonal rule. Therefore, the prediction precision at seasonal transition points and periodic peaks and valleys is obviously improved. According to the method, the characteristic that the SARIMA model is simple in structure is utilized, a reliable prediction model which is relatively low in historical data size requirement and not prone to overfitting is constructed, and stable output in practical application is ensured. According to the method, a set of standardized and reproducible prediction process is provided, so that each step of operation has a clear statistical basis, and the interpretability of a prediction result and the credibility of management personnel are enhanced. According to the method, a solution with excellent balance between prediction precision and calculation resource consumption is realized, so that the dual requirements of practical engineering application on timeliness and economy are met.

The invention discloses a fire-fighting system alarm method based on multi-sensor coupling data support utility index prediction, and relates to the technical field of fire-fighting system alarm, and the method comprises the steps: synchronously collecting real-time measurement data, and forming respective time sequences; carrying out normalization processing on various measurement data by adopting a minimum-maximum normalization method to obtain a normalized time sequence; an autoregressive integrated moving average model algorithm is adopted for the normalized time sequence, and normalized values of various parameters at the next moment in the future are predicted; and calculating a multi-sensor coupling data support utility index according to the predicted normalized values of various parameters at the next moment in the future, evaluating the coupling utility of the sensors, and triggering a fire behavior judgment strategy. According to the method, comprehensive identification of early characteristics of the fire is realized, so that the system can process abnormal data in a complex environment more stably, technical support is provided for upgrading of an intelligent fire-fighting system, and the method has engineering application value and popularization significance.

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 invention discloses a remote anti-electricity-stealing monitoring and alarming method based on artificial intelligence, and particularly relates to the field of power monitoring, which comprises the following steps: S1, multi-source data acquisition: acquiring user electricity consumption data and power grid operation data based on a preset frequency, S2, primary data processing: performing abnormal value processing, noise data processing and missing data processing on the acquired data, and S3, carrying out secondary data processing on the acquired data. S3, carrying out secondary data processing, carrying out standardization processing on numeric data after primary processing, and carrying out data coding on non-numeric data; S4, carrying out electricity consumption abnormity marking based on time sequence analysis, constructing an autoregression integral moving average model, predicting future electricity consumption power and comparing the future electricity consumption power with real-time power; S5, carrying out electricity stealing identification, and establishing a theoretical calculation model to calculate power deviation. And extracting harmonic characteristics to calculate a distortion rate, and comparing with a preset threshold to judge an electricity stealing behavior. Through multi-dimensional data acquisition and intelligent analysis, the anti-electricity-stealing identification precision is improved, and stable operation of a power system is effectively guaranteed.

A system for detecting knock using an autoregressive integrated moving average (ARIMA) model. The system includes an electronic processor. The electronic processor is configured to receive a signal from a knock sensor and determine components of the signal using an ARIMA model, where the components include a residual component. The electronic processor is further configured to determine whether combustion is knock or non-knock combustion based on a magnitude of the residual component.