141 results about "Time series dataset" patented technology

The information use frequency prediction device is provided with a comparison section for shifting a second time series data related to the use frequency of a first keyword, sequentially on the time base in a unit of predetermined time, based on a first time series data related to the use frequency of a second keyword input while searching over the Internet, and calculating a correlation coefficient between the first time series data and the second time series data, for each unit of predetermined time. Moreover, a prediction section specifies a pair of the first time series data and the second time series data, corresponding to the correlation coefficient having the highest value, of a plurality of correlation coefficients calculated by the comparison section and for predicting the use frequency of the first time series data constituting the pair, based on the second time series data.

A computer-implemented method, system, and a computer readable article of manufacture identify local patterns in at least one time series data stream. A data stream is received that comprises at least one set of time series data. The at least one set of time series data is formed into a set of multiple ordered levels of time series data. Multiple ordered levels of hierarchical approximation functions are generated directly from the multiple ordered levels of time series data. A set of approximating functions are created for each level. A current window with a current window length is selected from a set of varying window lengths. The set of approximating functions created at one level in the multiple ordered levels is passed to a subsequent level as a set of time series data. The multiple ordered levels of hierarchical approximation functions are stored into memory after being generated.

The invention provides a time series data storing and querying method and a storing and processing platform, and relates to the technical field of databases. The time series data storing and queryingmethod and the storing and processing platform are applied to a database, and the database comprises an indexing file and a data file. The time series data storing method comprises the following steps: receiving time series data to be stored and compressing the time series data to obtain compressed time series data; writing the compressed time series data to the tail of a data file; determining the offset, timestamp and data length of the compressed time series data; and when the compressed time series data are written successfully, constructing an index entry according to the timestamp, offset and data length, and writing the index entry to the tail of an index file. An embodiment of the invention aims to provide the time series data storing and querying method and the storing and processing platform so as to rapidly store, query and process a bulk of time series data, and time series database service which is high in disaster tolerance ability, convenient to use and efficient is provided.

A data analysis computer system is provided that receives a timeseries dataset and generates implied data from the dataset. The dataset is further vectorized to reduce the dimensionality of the data. Users provide input to identify windows of data that either positively or negatively correlate to instances of a given type of occurrence within the data. The user defined windows are converted to fixed sized windows and a machine learning algorithm constructs a model from the data. The model is used to predict instances of the given type of occurrence in newly received data. Validation of the predications may be performed.

The invention discloses a remote sensing time sequence analysis-based abandoned land information extraction method. The method comprises the following steps of obtaining remote sensing image information; constructing an NDVI time sequence data set according to the acquired remote sensing image information, and constructing an NDVI continuous time sequence growth change curve of vegetation in the cultivated land range; according to the NDVI continuous time sequence growth change curve of the vegetation in the cultivated land range, extracting the parameter CV of the fluctuation intensity of theintra-year change curve of the NDVI; and extracting spatial distribution information of the abandoned land from the cultivated land range according to the CV value. According to the method, the phenological characteristic difference analysis of the abandoned land and the cultivated land can be carried out by utilizing remote sensing intra-year and inter-year continuous time sequence images, and the growth characteristics of the abandoned land are represented by normalizing the CV of the intra-year change curve fluctuation intensity of the difference vegetation index NDVI, so that the distribution information of the abandoned land in the year is obtained; and aggregating the multi-year image time sequence set to extract multi-year abandoned land spatial distribution information, thereby obtaining multi-year abandoned land evolution information.

The invention relates to a vegetation index time series data reconstruction method based on wavelet multi-scale decomposition. The method is characterized by comprising the following steps: performing wavelet transformation according to the vegetation index time series data to respectively sequentially decompose the original time series data of vegetation indexes and climatic factors into corresponding half moth, month, double month, season, half a year, and time series data based on interannual scales; further selecting the climatic factor time series data under the corresponding scale according to the features of the vegetation index time series data in each scale; selecting a proper model; reconstructing time series data in different scales; finally combining the vegetation index time series data in all scales to realize the reconstruction of the original vegetation index time series data. The method has the characteristics of being high in precision and wide in applicable scope.

Systems and methods are provided for monitoring a subject, and particularly, for inferring an underlying brain state present in absence of current conditions. In some aspects, a method for monitoring the subject is provided including steps of receiving physiological feedback from at least one sensor configured to acquire physiological information from locations associated with a subject's brain, assembling a set of time-series data using the received physiological feedback, and identifying portions of the set of time-series data that indicate a burst suppression state. The method also includes identifying a burst characteristic profile associated with a burst pattern determined from the identified portions, and comparing the burst characteristic against a reference set of burst profiles. The method further includes determining, based on the comparison, a likelihood of a brain state of the subject underlying the burst suppression state, and generating a report indicative of the likelihood of the determined brain state.

A detecting apparatus includes the following units. A pressure detecting unit detects in time series higher and lower static pressures and a differential pressure. Fluctuation calculating units calculate time series data sets of a differential pressure fluctuation, a higher static pressure fluctuation, and a lower static pressure fluctuation. Square sum calculating units calculate the square sum of the time series data sets of the differential pressure fluctuation, the square sum of the time series data sets of the higher static pressure fluctuation, and the square sum of the time series data sets of the lower static pressure fluctuation. A correlation coefficient calculating unit calculates first and second correlation coefficients. An intermediate variable calculating unit calculates an intermediate variable. A blockage rate calculating unit calculates a blockage rate. A determination unit determines a blockage of higher and lower pressure impulse lines with reference to the blockage rate.

An aspect of synchronization of time between simulation models includes receiving a specification request for generating a set of target time-series data using a set of source time-series data, obtaining specification information relating to the set of source time-series data, obtaining specification information relating to the set of target time-series data and obtaining the source time-series data; comparing the source specification information and the target specification information to determine if the set of source time-series data are time-aligned with the set of target time-series data, and converting the set of source time-series data to the set of target time-series data upon determination that time alignment is needed.