50 results about "Time-series segmentation" patented technology

A learning motion control is made by using a hierarchical recurrent neural network. A variety of motion patterns can be created by segmenting a motion pattern taught by a human into parts, automatically in time series, combining the parts, and controlling the motion of the body. Local time-series patterns and a perspective pattern combination of the local time-series patterns are formed by time-series segmentation. The control stability of the motion pattern is ensured since the ZMP stability criteria witch are the indices of the static and dynamic stabilities of body are satisfied.

A method and system are provided. The method includes extracting shapelets from each of a plurality of time series dimensions of multi-dimensional time series data. The method further includes building a plurality of decision-tree classifiers, one for each time series dimension, responsive to the shapelets extracted therefrom. The method also includes generating a pairwise similarity matrix between respective different ones of the plurality of time series dimensions using the shapelets as intermediaries for determining similarity. The method additionally includes applying a feature selection technique to the matrix to determine respective feature weights for each of shapelet features of the shapelets and respective classifier weights for each of the decision-tree classifiers that uses the shapelet features. The method further includes combining decisions issued from the decision-tree classifiers to generate a final verdict of classification for a time series dimension responsive to the respective feature weights and the respective classifier weights.

Data processing method for analysis of the energy consumption of a site on the basis of measurements including:selection of data time series,segmentation of the time series into segments,projection of the segments,display of the projections of the data segments,e) selection of at least one first group of data segments,establishment of a digital model of classification characterising the first group of data segments, selected on the basis of calendar metadataestablishment of a digital model of classification characterising the first group of data segments, selected on the basis of the segments' profiles (FIG. 5B).

The invention discloses a life prediction method based on the change point detection of an associated synchronous time series signal, which is used to solve the life prediction problem under the interactive influence of large time scale and multi-dimensional time series. First, the original multi-dimensional synchronous time series is resampled to obtain the equivalent time series signal after scale reduction; secondly, a likelihood function is constructed for the sampled signal with the change point vector, piecewise slope and piecewise variance as parameters ; Then, for different values ​​of the number of change points, the maximum likelihood estimation is used to estimate the best value of the change point vector, piecewise slope and piecewise variance; The change point vector corresponding to the selected optimal number of change points divides the original time series into a plurality of local time series that are not constant zero and have only one change point, and repeatedly construct the likelihood function and In the step of optimal change point estimation, the final change point position of the original time series is obtained; finally, the signal at which the final change point is located is determined, and the time series at the tail of each dimension signal is obtained, which is used for parameter estimation required for subsequent life prediction.

The invention provides a dynamic system gain estimation method based on a historical data steady-state value, and the method comprises the steps: firstly, dividing an input time sequence and an outputtime sequence into short data segments through employing a linear segmentation representation method, and finding out data segments with input and output being in a steady state at the same time; calculating input and output steady-state values from the data segment under the steady-state condition; elements, in the steady-state values, associated with the same steady-state gain in statistical significance are found, the elements are divided into one group, the steady-state gain of each group is estimated, and interval estimation of estimation parameters is given. According to the method, theinput and output steady-state values can be automatically found in the historical data sample, a plurality of steady-state gains under different working conditions can be accurately and effectively estimated, verification is carried out through a visualization method, and the problems that the time consumption for searching the steady-state values is long, the steady-state values are easily influenced by nonlinearity and the steady-state gain change is difficult to detect are solved.

The invention discloses a spatial time sequence data stream application-based Skyline query method, belongs to the field of application of a dynamic Skyline query in a data stream, and aims to solve the problem in real-time query processing of massive data. According to the technical key points, the method comprises the steps of S1, segmenting a continuous time sequence into a plurality of time slices according to time windows based on spatial time sequence division; S2, for each time slice, generating a grid inverted index; and S3, mapping moment query points to corresponding Skyline grids, then obtaining global Skyline grids by using a global Skyline grid calculation method to serve as a candidate set, performing the dynamic Skyline query on network node data in the candidate set according to a time sequence, and performing calculation to obtain an effective global Skyline result. The method has the effects that the result query is performed at a moment when executive time is over, so that the result is more accurate and conforms to the actual condition.