SYSTEMS AND METHODS FOR CLASSIFICATION OF MULTI-DIMENSIONAL TIME SERIES OF PARAMETERS

摘要

#$%^&*AU2019201881A120200123.pdf#####James & Wells Ref: 311299AU ABSTRACT Traditional systems and methods have implemented hand-crafted feature extraction from varying length time series that results in complexity and requires 5 domain knowledge. Building classification models requires large labeled data and is computationally expensive. Embodiments of the present disclosure implement learning models for classification tasks in multi-dimensional time series by performing feature extraction from entity's parameters via unsupervised encoder and build a non-temporal linear classifier model. A fixed-dimensional feature 10 vector is outputted using a pre-trained unsupervised encoder, which acts as off-the shelf feature extractor. Extracted features are concatenated to learn a non-temporal linear classification model and weight is assigned to each extracted feature during learning which helps to determine relevant parameters for each class. Mapping from parameters to target class is considered while constraining the linear model 15 to use only subset of large number of features. [To be published with FIG. 2] 28Obtaining a unique time series data corresponding to a plurality of parameters of one or more entities 202 Automatically extracting, using an unsupervised encoder integrated within a Deep Recurrent Neural Network (RNN), one or more features from the unique time series to obtain a unique features set for each of the plurality of parameters, 204 wherein the unique features set comprises a fixeddimensional feature vector Concatenating the one or more extracted features from the unique features set pertaining each of the plurality of 206 parameters to obtain a concatenated features set comprising a fixed-dimensional concatenated feature vector Learning a non-temporal linear classification model based on the concatenated features set, wherein during the learning of the non-temporal linear classification model a 208 weight is assigned to each feature from the concatenated features set Generating a relevance score for each parameter based on the weight of each feature from the concatenated features 210 set to validate the learned non-temporal linear classification model Receiving an input time series corresponding to the plurality 212 of parameters of the entities Automatically extracting one or more features from the input 214 time series Applying the validated learned classification model on the input time series based on the extracted one or more 216 features to obtain a class forthe input time series corresponding to the plurality of parameters of the entities FIG. 2