anomaly-detection

问题 The goal is to say: "These values lie within a band of 95 % of values around the mean in a normal distribution." Now, I am trying to convert percentage to z-score, so then I can get the precise range of values. Something like <lower bound , upper bound> would be enough. So I need something like double z_score(double percentage) { // ... } // ... // according to https://en.wikipedia.org/wiki/68–95–99.7_rule z_score(68.27) == 1 z_score(95.45) == 2 z_score(99.73) == 3 I found an article

问题 The goal is to say: "These values lie within a band of 95 % of values around the mean in a normal distribution." Now, I am trying to convert percentage to z-score, so then I can get the precise range of values. Something like <lower bound , upper bound> would be enough. So I need something like double z_score(double percentage) { // ... } // ... // according to https://en.wikipedia.org/wiki/68–95–99.7_rule z_score(68.27) == 1 z_score(95.45) == 2 z_score(99.73) == 3 I found an article

来源： https://stackoverflow.com/questions/63175802/how-can-generate-impulse-as-outliers-on-periodic-or-sequenced-based-data-for-doi

来源： https://stackoverflow.com/questions/63175802/how-can-generate-impulse-as-outliers-on-periodic-or-sequenced-based-data-for-doi

问题 The data bellow shows part of my dataset, that is used to detect anomalies describe_file data_numbers index 0 gkivdotqvj 7309.0 0 1 hpwgzodlky 2731.0 1 2 dgaecubawx 0.0 2 3 NaN 0.0 3 4 lnpeyxsrrc 0.0 4 I used One Class SVM algorithm to detect anomalies from pyod.models.ocsvm import OCSVM random_state = np.random.RandomState(42) outliers_fraction = 0.05 classifiers = { 'One Classify SVM (SVM)':OCSVM(kernel='rbf', degree=3, gamma='auto', coef0=0.0, tol=0.001, nu=0.5, shrinking=True, cache_size

问题 I build a simple LSTM model in Keras as below: model = Sequential() model.add(keras.layers.LSTM(hidden_nodes, input_dim=num_features, input_length=window, consume_less="mem")) model.add(keras.layers.Dense(num_features, activation='sigmoid')) optimizer = keras.optimizers.SGD(lr=learning_rate, decay=1e-6, momentum=0.9, nesterov=True) When I apply the model on some data I have this particular behaviour: Where the orange line represents the predicted values and the blue one the grand truth. As

问题 I read the following article Anomaly detection with Principal Component Analysis (PCA) In the article is written following: • PCA algorithm basically transforms data readings from an existing coordinate system into a new coordinate system. • The closer data readings are to the center of the new coordinate system, the closer these readings are to an optimum value. • The anomaly score is calculated using the Mahalanobis distance between a reading and the mean of all readings, which is the

问题 I have a question about Calculating anomaly score for Anomaly detection using One-Class SVM. My question is that: How can I calculate it using decision_function(X), just the way I calculate anomaly score in Isolation forest? Thanks a lot, 回答1: In Isolation Forests, anomaly score is a measure of the deviation of average length of the path required to single out a particular observation from the average length of path required to single out a "normal" observation The average here is taken over

问题 I tried to use AnomalyDetectionTs() by library(AnomalyDetection) from https://github.com/twitter/AnomalyDetection and https://www.r-bloggers.com/anomaly-detection-in-r/ on my data. In my example data, there are very swing values without dropping curve (or dropping slowly like pattern) on plot more than it should be from its pattern. This function doesn't work for me. All those anomaly detected points by the function are right and normal values. This is the result from the function : My

问题 Anomaly detection methods published and now abandoned by twitter have been separately forked and maintained in the anomalize package and the hrbrmstr/AnomalyDetection fork. Both have implemented features that are 'tidy'. Working static versions tidyverse_cran_downloads %>% filter(package == "tidyr") %>% ungroup() %>% select(-package) -> one_package_only one_package_only %>% anomalize::time_decompose(count, merge = TRUE, method = "twitter", frequency = "7 days") -> one_package_only_decomp one