44 results about "Granger causality" patented technology

The present invention relates to a method and device for analyzing the root cause of IT failures by using temporal correlation, wherein the method includes: obtaining system logs; extracting key fields of the system logs, and performing statistics on the key fields to obtain Obtain the time series data of the system log; automatically extract the relevant features of the time series data based on quantitative hypothesis detection; when an IT failure occurs, test the relevant features of the time series data through Granger causality, wherein, The magnitude of the causal relationship between the relevant features of the time series data is used as the basis for evaluating the cause of the IT failure. The beneficial effects of the present invention are: automatically complete the fault root cause analysis process through machine learning, help users quickly find out the cause of the fault, reduce the fault diagnosis time (Mean Time To Diagonise, MTTD), and make the system return to normal as soon as possible.

The invention provides a method for predicting the key industrial electricity consumption based on an industrial condition index. The method comprises the following steps: (1) obtaining the key industrial condition index and historical electricity consumption data; (2) performing seasonal adjustment and a stationary test on the data; (3) judging whether the industrial condition index and the industrial electricity consumption have a causal relationship or not through a Granger causality test and determining an optimal lag period of the condition index; (4) creating a time sequence ARIMA (autoregressive integrated moving average) model of the key industrial electricity consumption, introducing the key industrial condition index into an original ARIMA model, and creating a regressive model; (5) on the basis of an AIC (Akaike information criterion), screening out an optimal model; (6) performing model popularization and application, and predicting the industrial electricity consumption in the future. The key industrial electricity consumption is taken as a study object, the electricity consumption and the influence of the industrial condition index on the electricity consumption are studied by introducing the industrial condition index, the key industrial electricity consumption is accurately predicted in combination with the time sequence model, and a basis is provided for development and planning of electricity industry in the future.

A conjoint analysis method for electroencephalograph and electromyography signals based on autonomous movement and imagination movement comprises the steps of performing system setup, and using a LabVIEW 8.6 to generate square wave pulse signals; respectively collecting electroencephalograph signals and electromyography signals including electroencephalograph signals and electromyography signals in autonomous movement modalities and in imagination movement modalities; performing noise removal pretreatment on collected original data; performing electroencephalograph and electromyography time-domain signal analysis in the autonomous movement and imagination movement modalities on electroencephalograph and electromyography signal time-domain pictures which are performed with noise removal pretreatment in the autonomous movement and imagination movement modalities; performing time-frequency signal analysis on electroencephalograph and electromyography signals performed with noise removal pretreatment and in the autonomous movement and imagination movement modalities based on Morlet wavelet transformation; and performing partial directional coherence analysis, and in particular adopting granger causality to perform the partial directional coherence analysis. The conjoint analysis method provides new evaluation parameters for monitoring recovery auxiliary equipment and assessing organism movement level.