Sktime is a popular new python package for time series machine learning. The contributors continue to fix bugs and add new features — and invite you to contribute too!
Why contribute to
The community is particularly motivated to support new and/or anxious contributors. People who are looking to learn and develop their skills are welcomed and supported.
Community members of all experience levels are invited to the…
Most state-of-the-art (SOTA) time series classification methods are limited by high computational complexity. This makes them slow to train on smaller datasets and effectively unusable on large datasets.
Recently, ROCKET (RandOM Convolutional KErnel Transform) has achieved SOTA of accuracy in just a fraction of the time as other SOTA time series classifiers. ROCKET transforms time series into features using random convolutional kernels and passes the features to a linear classifier.
MiniRocket is even faster!
MiniRocket (MINImally RandOm Convolutional KErnel Transform) is a (nearly) deterministic reformulation of Rocket that is 75 times faster on larger datasets and boasts roughly equivalent accuracy.
So you want to write a widely read article about Data Science / Machine Learning / Artificial Intelligence?
In May 2021, I was recognized as a top writer in AI and was among the top 1000 writers in the Medium Partner Program. My older articles still continue to receive views and often appear in Google searches. (Scroll to the bottom for a screenshot of my stats).
Read along to learn some of the keys to my success.
I first learned about Medium as a data scientist searching for specific topics in Data Science. …
Outlier detection is a machine learning task that aims to identify rare items, events, or observations that deviate from the “norm” or general distribution of the given data.
An anomaly is something that arouses suspicion that it was generated by different data generating mechanism
In the outlier detection task, the goal is train an unsupervised model to find anomalies subject to two constraints:
In many applications, there is a third constraint: the “ground truth” of what are true…
PyOD is a Python library with a comprehensive set of scalable, state-of-the-art (SOTA) algorithms for detecting outlying data points in multivariate data. This task is commonly referred to as Outlier Detection or Anomaly Detection.
The outlier detection task aims to identify rare items, events, or observations that deviate from the “norm” or general distribution of the given data.
My favorite definition: An anomaly is something that arouses suspicion that it was generated by different data generating mechanism
Common applications of outlier detection include fraud detection, data error detection, intrusion detection in network security, and fault detection in mechanics.
Outliers, or anomalies are data points that deviate from the norm of a dataset. They arouse suspicion that they were generated by a different mechanism.
Anomaly detection is (usually) an unsupervised learning task where the objective is to identify suspicious observations in data. The task is constrained by the cost of incorrectly flagging normal points as anomalous and failing to flag actual anomalous points.
Applications of anomaly detection include network intrusion detection, data quality monitoring, and price arbitrage in financial markets.
Copula-Based Outlier Detection — COPOD — is a new algorithm for anomaly detection. …
The world is inherently dynamic and nonstationary — constantly changing.
It is common for the performance of machine learning models to decline over time. This occurs as data distributions and target labels (“ground truth”) evolve. This is especially true for models related to people.
Thus, an essential component of machine learning systems is monitoring and adapting to such changes.
In this article, I will introduce this idea of concept drift or regime change and then discuss three ways to handle it and what you should consider.
New tools for model monitoring are emerging, but it is still important to understand…
Clustering is an unsupervised learning task where an algorithm groups similar data points without any “ground truth” labels. Clustering different time series into similar groups is a challenging because each data point is an ordered sequence.
In a previous article, I explained how the k-means clustering algorithm can be adapted to time series by using Dynamic Time Warping, which measures the similarity between two sequences, in place of standard measures like Euclidean distance.
Unfortunately, the k-means clustering algorithm for time series can be very slow!
Hierarchical clustering is faster than k-means because it operates on a matrix of pairwise distances…
“The task of time series classification can be thought of as involving learning or detecting signals or patterns within time series associated with relevant classes.” — Dempster, et al 2020, authors of ROCKET paper
Most time series classification methods with state-of-the-art (SOTA) accuracy have high computational complexity and scale poorly. This means they are slow to train on smaller datasets and effectively unusable on large datasets.
Data scientist working in the financial services industry