final double[] discretizedFeatures = new double[lp.features().size()]; for (int i = 0; i < lp.features().size(); ++i) { discretizedFeatures[i] = Math.floor(lp.features().apply(i) / 16); } return new LabeledPoint(lp.label(), Vectors.dense(discretizedFeatures)); } }); // C...
tsfel.feature_extraction.calc_features.time_series_features_extractor ...返回：提取的特征 返回类型：...
Problem: global features and local features of time series data are extracted correctly. A time series feature extractorA coefficient output part that outputs coefficients for computation to classify time series data into multiple segments andBased on the coefficients, we classify time series data into...
time_series_features_extractor(cfg, df)Available featuresStatistical domainFeaturesComputational Cost ECDF 1 ECDF Percentile 1 ECDF Percentile Count 1 ECDF Slope 1 Histogram 1 Interquartile range 1 Kurtosis 1 Max 1 Mean 1 Mean absolute deviation 1 Median 1 Median absolute deviation 1 Min 1 Root ...
When deploying machine learning applications, a proper set of features can improve the performance of the algorithms and reduce the computational complexity. In the signal windowing step, time series are divided into user-defined fixed length time windows (which can optionally have some overlap), fro...
Features, the topic of the following work, are numerical descriptors that aim to characterize and distinguish the different variability classes. They can go from basic statistical measures such as the mean or the standard deviation, to complex time-series characteristics such as the autocorrelation ...
Context-FIDis an evaluation measure that aims to quantify the similarity of real and synthetic time series distributions following the example of the existing FID score for image synthesis. Specifically, this approach replaces the image feature extractor of regular FID, InceptionV3 [47], with the ...
3)backpropagationto turn the first few layers into an appropriate feature extractor 4) a fully-connected first layer with, saya few 100 hidden units, would already contain several10,000 weights 5) learning these weight configurationsrequires a very large number of training instancesto cover the sp...
This work has a potentially large impact, as it enables classification of EEG data based on multivariate time series analysis in an interpretable way with high accuracy. The method allows a model with a reduced number of features, facilitating its interpretability and improving overfitting. Future wo...
feature extraction is arduous because intrinsic features of time series data are challenging to capture. For this reason, distance-based approaches are more successful in classifying multivariate time series data [17]. Hidden State Conditional Random ...