53 results about "Time series data analysis" patented technology

A time-series data analyzing apparatus which extracts a composite factor time-series pattern from time-series data. The apparatus includes a dividing device which divides the time-series data into pattern generation time-series data and pattern inspection time-series data which do not include pattern generation time-series data. A first generating device generates a transitional pattern including a support time data indicating a transition of support time and having a transition occurrence probability higher than a minimum occurrence probability in the pattern generation time-series data. A second generating device generates frequently appearing integrated transitional patterns. A second computing device computes cause-and-effect strength of each of the frequently appearing integrated transitional patterns using the pattern inspection time-series data. A display device displays the composite factor time-series pattern having the cause-and-effect strength higher than the minimum cause-and-effect strength given preliminarily.

A time series data analyzer includes a segment condition input section, an analysis condition input section, and an optimum condition deriving section for analyzing all segments based on the segment conditions and analysis conditions inputted in the respective input sections, under all analysis conditions by a maximum entropy method and a nonlinear least squares method. The time series data analyzer derives the optimum segment length and the optimum lag value in correspondence to selected results, and an analysis execution section executes analysis by the maximum entropy method by setting the optimum analysis conditions derived as described above. The trending of the spectrum of electroencephalogram data is used as an indicator of the state of the subject based on the findings that the spectrum of electroencephalogram data is an exponential spectrum and the gradient changes depending on the state of the subject.

A text data storage unit stores a plurality of text data having attribute data and time data. A dictionary storage unit stores a plurality of events each associated with text data. An analysis condition indication unit indicates an analysis target as attribute data and an analysis condition as an event sequence. A time series data generation unit assigns an event to each of the plurality of text data by referring to the dictionary storage unit, extracts a group of text data each having the same attribute data as the analysis target from the plurality of text data, and generates time series data each representing the event assigned to the text data of the group in order of the time data of the text data. A time series data analysis unit analyzes the time series data each having the same event sequence as the analysis condition.

The invention discloses a similarity measurement computing method of linear dynamical systems. The method comprises the following steps that time series data to be processed are collected; linear dynamical system model parameters corresponding to the time series data are respectively computed; subspace included angles between the corresponding linear dynamical systems are obtained by computing the linear dynamical system model parameters; frame offset optimization is carried out on the subspace included angles to obtain the similarity measurement of the time series data. The method can be applied to time series data analysis, video content retrieval, intelligent video monitoring and other services.

The invention discloses a time series data motif identification method and device, and belongs to the field of time series data analysis. The time series data motif identification method comprises the steps that time series data needing to be analyzed are divided into at least two data subsequences, and each data subsequence is converted into a symbol subsequence; random projection is performed on the symbol subsequences, and the times that each projected symbol subsequence and other projected symbol subsequences have same signals are recorded; two data subsequences which are corresponding to the time exceeding a threshold value in the recorded times and between which the distance is smaller than a first preset distance serve as identified standard motifs; clustering is performed on the standard motifs in each preset range to obtain a center data subsequence, and the variance of each preset range is calculated according to the standard motifs in each preset range and the center data subsequence; the threshold value is decreased, the distances between two data subsequences which are corresponding to the time exceeding the decreased threshold value in the recorded times and the center data subsequence in the preset range where the two data subsequences are located, and the data subsequence of which the distance is smaller than the variance of the preset range where the data subsequence is located serves as the identified motif. According to the time series data motif identification method and device, under the condition that the motif identification speed is guaranteed, the motif identification accuracy can be improved.

A plurality of analytics in a cloud-based environment is accessed. Each of the plurality of analytics performs an operation on time series data. Within the cloud-based environment, a selected one or more of the plurality of analytics is chosen. A set of time series data is uploaded to the cloud-based environment and the selected one of the plurality of analytics is optimized on that set of time series data.