50 results about "Time series similarity" patented technology

The invention discloses a multi-measurement time series similarity analysis method applicable to k-neighbor inquires of a time series. A multi-single-similarity-measurement method is chosen according to the analysis requirement, each single similarity measurement is used to analyze and inquire an m-neighbor sequence or subsequence of the sequence, pruning the m-neighbor sequence or subsequence under each similarity measurement to obtain a candidate similarity sequence or subsequence, and combining the candidate similarity sequence or subsequence by using a multiple-classifier combination method with advantage weight to obtain the k-neighbor sequence of the inquired sequence. Compared with the single similarity measurement, the similarity analysis of combined multiple measurements can obtain a more comprehensive analysis result. The multiple-classifier combination method with advantage weight regulates the ranking score according to the difference of the similarity distance between the adjacent candidate similarity sequence or subsequence and the inquired sequence while using a BORDA counting method for reference, so as to reflect the specific difference of similarity of the candidate similarity sequence or subsequence.

A location estimation method using label propagation. The achieved location estimation method is robust to variations in radio signal strengths and is highly accurate by using the q-norm (0<q<1), especially, for calculating the similarities among radio signal strength vectors. The accuracy in location estimation is further improved by putting more importance on the time-series similarities. Specifically, the time-series similarity is calculated by using time-series values indicating the temporal order of radio signal strengths during the measurement. If the time-series similarity is larger than the similarity between the radio signal strength vectors, the time-series similarity is preferentially used. The exponential attenuation function can also be used for calculating the similarities, instead of the q norm (0<q<1).

The invention discloses a time-series similarity measurement method based on segmented statistical approximate representation. The method comprises the steps of feature extraction and dynamic pattern matching. First, a time series is segmented into sub series, the various statistical features of the sub series are sequentially extracted, and local pattern feature vectors are constructed; then the distance between the local pattern feature vectors is calculated by the weighted Euclidean distance, local pattern matching is achieved, the matched local pattern is used as the sub program of a dynamic programming algorithm, and global pattern matching is achieved. The method is superior to other measurement methods by a large degree on the aspects of measurement precision and calculation efficiency, and plays an important role in daily activities and industrial production of people, for example, financial transactions, traffic control, air quality and temperature monitoring, industrial flow monitoring, medical diagnosis and the like. Large scale sampling data or high-speed dynamic data flow is subjected to similarity-based search, classification, clustering, prediction, abnormal detection, on-line pattern recognition and the like.

The invention relates to a vegetation change occurrence time detection method based on time series similarity, comprising: first, establishing multi-year time-space continuous vegetation index time series data of a research area, calculating JM distance between vegetation index time series curves of each past year and the initial year pixel by pixel, year by year, and generating a time series curve for the JM distance of each past year and the original year; fitting the time series curve for the JM distance of each past pear and the original year by using a logistic model, and acquiring time parameter from logistic model parameters so as to automatically extract vegetation change time. In the method, the JM distances of vegetation index time series curves between the past years and the initial year are used to indicate time series similarity, and vegetation change time is acquired from change law of yearly time series similarity. The method is effective in detecting changes of time series curves in terms of amplitude, frequency and the like, the complex step of decomposing original spectral index time series data is avoided, and the problem that it is difficult to extract indexes directly from original spectral index time series data to provide comprehensive characterization of vegetation changes is solved.

The invention is suitable for the performance data analyzing field and provides a performance data dependency analyzing method and a performance monitoring system. The method comprises: a step of collecting performance data of a plurality of indexes and storing the performance data to build a database; a step of extracting a performance data of one index in the database and building a time series according to a time collecting sequence; a step of extracting a graphic feature vector of the time series; a step of building a graphic feature index for the graphic feature vector; a step of building an object time series; a step of extracting a target image feature vector of the object time series; and a step of querying the graphic feature vector similar to the target graphic feature vector in the graphic feature index, and ranking and outputting the queried result. According to the invention, the method can realize big data analytics of the performance data via a time series similarity search technology, so that the variable characteristics on a timer shaft can be intuitively reflected and a manager can directly analyze the different factors of generating the performance problems via the queried result.

The invention discloses a trend segmentation similarity-based airport noise monitoring point exception identification method and belongs to the technical field of airport noise monitoring point exception analysis. The method comprises the steps of firstly obtaining noise monitoring data of monitoring points around an airport by utilizing a monitoring device; secondly preprocessing the monitoring data and creating a standard noise time series data set; thirdly performing dimension reduction expression on noise time series of the monitoring points by using a trend segmentation-based time series expression method; fourthly by utilizing a trend segmentation-based similarity measurement method, measuring noise time series similarity between the monitoring points, and establishing a similarity matrix; fifthly finding out first k monitoring points with relatively high similarity with each monitoring point, and creating a similar monitoring point set; and finally measuring the similarity between new noise time series of the monitoring points and new noise time series of associated monitoring points, and if the similarity is remarkably changed, determining that the monitoring points are exceptional. According to the method, the monitoring point exception can be accurately identified, so that the timeliness and validity of airport noise monitoring point maintenance are effectively improved.

The invention discloses a time series similarity searching method based on segment weight, which comprises the following steps: (1) segmenting a query sequence q by adopting an important turning pointof the time series; (2) establishing piecewise Euclidean distance with weights as similarity measure of time series; (3) performing k-nearest neighbor similarity querying on the q, searching for thek most similar sequences of the query sequence q; (4) ending the query if the user is satisfied with the query result obtained in the step (3); If the user is not satisfied with the query result obtained in the step (3), marking the result and entering the step (5); (5) allowing the system to update the weights of the segments by using the sequence of user tags and returning to step (2). The invention automatically updates the weights through user feedback, adaptively learns the attention degree of the user to different segments, can improve the accuracy of similarity measurement, and furtheroptimizes the search result.

The invention relates to a multivariate time series similarity measuring method oriented to the ocean field. The similarity measuring method comprises the following steps that S1, typhoon data is collected; S2, the typhoon data is preprocessed, wherein typhoon attribute screening and data supplementing are included; S3, the typhoon data is described, wherein movement direction showing and typhoon time series showing are included; S4, the typhoon data is subjected to similarity measurement, wherein typhoon attribute weight calculation, W-DTW distance calculation and W-DTW distance judgment are included ; S5, similar typhoon is output. The method has the advantages that whether two ocean time series with dynamics, spatiality, predictability and multiple attributes are similar or not is judged; the development trend of a current ocean event is judged based on an occurring ocean event; for ocean disasters, a convenient auxiliary decision can be provided for relevant departments, protection measures are taken, and economic losses and casualties caused by the ocean disasters are reduced.

The invention discloses a time sequence similarity calculation method and system based on deep learning, and a medium. The time sequence similarity calculation method based on deep learning comprisesthe implementation steps of 1) obtaining time sequence data of two equal-length time periods; and 2) inputting the time series data of the two equal-length time periods into a pre-trained neural network model based on deep learning to obtain the similarity between the time series data of the two equal-length time periods. According to the invention, the advantages of various traditional measurement methods are integrated; the effect is better than that of each traditional measurement method in the aspect of time sequence similarity measurement; according to different requirements and differentdata sets, the same method can be used to learn a data similarity measurement method suitable for different fields, and for different problems, a similarity calculation method does not need to consider inherent characteristics of data.

The invention discloses a time series similarity measuring method. The method combines a dynamic time bending algorithm and a derivative dynamic time bending algorithm, increases the accuracy of timeseries similarity measurement, and provides a solid foundation for the subsequent research of time series.