This is possible splitting the time-series into equal sized pieces and smoothing them independently. As always, this functionality is implemented in a vectorized way through the WindowWrapper class.

How lenient the outlier detection program is when counting outliers. That is, the algorithm counts all outliers within +/- margin from the time-series data point in question, and if the number of ...

PyOD is a flexible and scalable toolkit designed for detecting outliers or anomalies in multivariate data; hence the name PyOD (Python Outlier Detection).It was introduced by Yue Zhao, Zain Nasrullah ...

Anomaly detection is an important part of machine learning that makes the results unbiased to any category or class. While in time series modelling it takes a very important place because there is a ...

This paper proposed the combination of two statistical techniques for the detection and imputation of outliers in time series data. An autoregressive integrated moving average with exogenous inputs ...

Darts is Python library that aims to be the scikit-learn for time series analysis. By providing a unified and consistent API, Darts simplifies the end-to-end process of working with time series data.

Time series pattern outlier represents a pattern with abnormal behavior that is significantly different from other patterns in the time series and can induce a bias in the decision-making process ...