Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …

tslearn is a general-purpose Python machine learning library for time series that offers tools
for pre-processing and feature extraction as well as dedicated models for clustering …

In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31 (3): 606-
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …

Time series feature extraction is one of the preliminary steps of conventional machine
learning pipelines. Quite often, this process ends being a time consuming and complex task …

We present sktime--a new scikit-learn compatible Python library with a unified interface for
machine learning with time series. Time series data gives rise to various distinct but closely …

Aberrant social behavior is a core feature of many neuropsychiatric disorders, yet the study
of complex social behavior in freely moving rodents is relatively infrequently incorporated …

Sensor data quality plays a vital role in Internet of Things (IoT) applications as they are
rendered useless if the data quality is bad. This systematic review aims to provide an …

In the face of a growing global population, plant breeding is being used as a sustainable tool
for increasing food security. A wide range of high-throughput omics technologies have been …

pyts is an open-source Python package for time series classification. This versatile toolbox
provides implementations of many algorithms published in the literature, preprocessing …