Related papers: FATS: Feature Analysis for Time Series
Time-series stationarity is a property that statistical characteristics such as trend, variance, seasonality remain constant over time. It is considered fundamental to many forecasting and analysis methods. Different tests detect different…
Actionable analytics are those that humans can understand, and operationalize. What kind of data mining models generate such actionable analytics? According to psychological scientists, humans understand models that most match their own…
We have implemented an extension for the observational seismology obspy software package to provide a streamlined tool tailored to the processing of seismic signals from non-earthquake sources, in particular those from deforming systems…
The ATLAS Fast TracKer (FTK) was designed to provide full tracking for the ATLAS high-level trigger by using pattern recognition based on Associative Memory (AM) chips and fitting in high-speed field programmable gate arrays. The tracks…
Recent research demonstrates that linear models achieve forecasting performance competitive with complex architectures, yet methodologies for enhancing linear models remain underexplored. Motivated by the hypothesis that distinct time…
This paper introduces new methodology based on the field of Topological Data Analysis for detecting anomalies in multivariate time series, that aims to detect global changes in the dependency structure between channels. The proposed…
Time series data mining is an important field of research in the era of "Big Data". Next generation astronomical surveys will generate data at unprecedented rates, creating the need for automated methods of data analysis. We propose a…
Multifractal detrended fluctuation analysis (MFDFA) has become a central method to characterise the variability and uncertainty in empiric time series. Extracting the fluctuations on different temporal scales allows quantifying the strength…
SimOutUtils is a suite of MATLAB/Octave functions for studying and analyzing time series-like output from stochastic simulation models. More specifically, SimOutUtils allows modelers to study and visualize simulation output dynamics,…
We introduce supervised feature ranking and feature subset selection algorithms for multivariate time series (MTS) classification. Unlike most existing supervised/unsupervised feature selection algorithms for MTS our techniques do not…
Climate change stands as one of the most pressing global challenges of the twenty-first century, with far-reaching consequences such as rising sea levels, melting glaciers, and increasingly extreme weather patterns. Accurate forecasting is…
Correlated time series (CTS) forecasting plays an essential role in many practical applications, such as traffic management and server load control. Many deep learning models have been proposed to improve the accuracy of CTS forecasting.…
Event-based cameras have recently drawn the attention of the Computer Vision community thanks to their advantages in terms of high temporal resolution, low power consumption and high dynamic range, compared to traditional frame-based…
AOtools is a Python package which is open-source and aimed at providing tools for adaptive optics users and researchers. We present version 1.0 which contains tools for adaptive optics processing, including analysing data in the pupil…
Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Time Series (NIRTS) and Accidental Irregular Time Series…
This is a companion Focus Demonstration article to the PyMUSE python package, demonstrating its usage and utilities for VLT/MUSE data analysis, that include a wide range of options for spectra extractions, the creation of different types of…
Modern time series analysis demands frameworks that are flexible, efficient, and extensible. However, many existing Python libraries exhibit limitations in modularity and in their native support for irregular, multi-source, or sparse data.…
Feature selection is an important and active field of research in machine learning and data science. Our goal in this paper is to propose a collection of synthetic datasets that can be used as a common reference point for feature selection…
When exoplanets pass in front of their stars, they imprint a transit signature on the stellar light curve which to date has been assumed to be symmetric in time, owing to the planet being modelled as a circular area occulting the stellar…
We introduce NightPulse, an interactive tool for Night-time light (NTL) data visualization and analytics, which enables researchers and stakeholders to explore and analyze NTL data with a user-friendly platform. Powered by efficient system…