A Comprehensive Python Library for Deep Learning-Based Event Detection in Multivariate Time Series Data and Information Retrieval in NLP

Event detection in time series data is crucial in various domains, including finance, healthcare, cybersecurity, and science. Accurately identifying events in time series data is vital for making informed decisions, detecting anomalies, and predicting future trends. Despite extensive research exploring diverse methods for event detection in time series, with deep learning approaches being among the most advanced, there is still room for improvement and innovation in this field. In this paper, we present a new deep learning supervised method for detecting events in multivariate time series data. Our method combines four distinct novelties compared to existing deep-learning supervised methods. Firstly, it is based on regression instead of binary classification. Secondly, it does not require labeled datasets where each point is labeled; instead, it only requires reference events defined as time points or intervals of time. Thirdly, it is designed to be robust by using a stacked ensemble learning meta-model that combines deep learning models, ranging from classic feed-forward neural networks (FFNs) to state-of-the-art architectures like transformers. This ensemble approach can mitigate individual model weaknesses and biases, resulting in more robust predictions. Finally, to facilitate practical implementation, we have developed a Python package to accompany our proposed method. The package, called eventdetector-ts, can be installed through the Python Package Index (PyPI). In this paper, we present our method and provide a comprehensive guide on the usage of the package. We showcase its versatility and effectiveness through different real-world use cases from natural language processing (NLP) to financial security domains.Comment: 2023 International Conference on Machine Learning and Applications (ICMLA

Event Detection in Time Series: Universal Deep Learning Approach

Event detection in time series is a challenging task due to the prevalence of imbalanced datasets, rare events, and time interval-defined events. Traditional supervised deep learning methods primarily employ binary classification, where each time step is assigned a binary label indicating the presence or absence of an event. However, these methods struggle to handle these specific scenarios effectively. To address these limitations, we propose a novel supervised regression-based deep learning approach that offers several advantages over classification-based methods. Our approach, with a limited number of parameters, can effectively handle various types of events within a unified framework, including rare events and imbalanced datasets. We provide theoretical justifications for its universality and precision and demonstrate its superior performance across diverse domains, particularly for rare events and imbalanced datasets.Comment: To be submitted to ICML 202

The Comet Interceptor Mission

Here we describe the novel, multi-point Comet Interceptor mission. It is dedicated to the exploration of a little-processed long-period comet, possibly entering the inner Solar System for the first time, or to encounter an interstellar object originating at another star. The objectives of the mission are to address the following questions: What are the surface composition, shape, morphology, and structure of the target object? What is the composition of the gas and dust in the coma, its connection to the nucleus, and the nature of its interaction with the solar wind? The mission was proposed to the European Space Agency in 2018, and formally adopted by the agency in June 2022, for launch in 2029 together with the Ariel mission. Comet Interceptor will take advantage of the opportunity presented by ESA's F-Class call for fast, flexible, low-cost missions to which it was proposed. The call required a launch to a halo orbit around the Sun-Earth L2 point. The mission can take advantage of this placement to wait for the discovery of a suitable comet reachable with its minimum ΔV capability of 600 ms-1. Comet Interceptor will be unique in encountering and studying, at a nominal closest approach distance of 1000 km, a comet that represents a near-pristine sample of material from the formation of the Solar System. It will also add a capability that no previous cometary mission has had, which is to deploy two sub-probes - B1, provided by the Japanese space agency, JAXA, and B2 - that will follow different trajectories through the coma. While the main probe passes at a nominal 1000 km distance, probes B1 and B2 will follow different chords through the coma at distances of 850 km and 400 km, respectively. The result will be unique, simultaneous, spatially resolved information of the 3-dimensional properties of the target comet and its interaction with the space environment. We present the mission's science background leading to these objectives, as well as an overview of the scientific instruments, mission design, and schedule

The Comet Interceptor Mission

Here we describe the novel, multi-point Comet Interceptor mission. It is dedicated to the exploration of a little-processed long-period comet, possibly entering the inner Solar System for the first time, or to encounter an interstellar object originating at another star. The objectives of the mission are to address the following questions: What are the surface composition, shape, morphology, and structure of the target object? What is the composition of the gas and dust in the coma, its connection to the nucleus, and the nature of its interaction with the solar wind? The mission was proposed to the European Space Agency in 2018, and formally adopted by the agency in June 2022, for launch in 2029 together with the Ariel mission. Comet Interceptor will take advantage of the opportunity presented by ESA’s F-Class call for fast, flexible, low-cost missions to which it was proposed. The call required a launch to a halo orbit around the Sun-Earth L2 point. The mission can take advantage of this placement to wait for the discovery of a suitable comet reachable with its minimum ΔV capability of 600 ms−1. Comet Interceptor will be unique in encountering and studying, at a nominal closest approach distance of 1000 km, a comet that represents a near-pristine sample of material from the formation of the Solar System. It will also add a capability that no previous cometary mission has had, which is to deploy two sub-probes – B1, provided by the Japanese space agency, JAXA, and B2 – that will follow different trajectories through the coma. While the main probe passes at a nominal 1000 km distance, probes B1 and B2 will follow different chords through the coma at distances of 850 km and 400 km, respectively. The result will be unique, simultaneous, spatially resolved information of the 3-dimensional properties of the target comet and its interaction with the space environment. We present the mission’s science background leading to these objectives, as well as an overview of the scientific instruments, mission design, and schedule

Responsabilités des professionnels de santé

International audienc

Responsabilités des professionnels de santé

International audienc

Droits des patients et des résidents des établissements sanitaires et médico-sociaux

International audienc

Droits des patients et des résidents des établissements sanitaires et médico-sociaux

International audienc

Universal Event Detection in Time Series

Event detection in time series data is a crucial task spanning various domains, and extensive research has explored methods to achieve this goal. These methods range from traditional threshold-based techniques to more advanced deep learning approaches. However, a comprehensive survey of existing methods reveals that each approach has its limitations, often lacking mathematical validation and exhibiting limited robustness. To address these limitations, this paper introduces a novel framework rooted in universal approximation theory, a well-established and proven methodology. This framework showcases the capability to accurately detect a broad spectrum of events in multivariate time series data with the desired precision. To bolster robustness, the proposed framework employs a stacked ensemble learning meta-model, effectively mitigating individual model weaknesses and biases, thereby yielding more resilient predictions. Moreover, this paper provides a user-friendly quick-start guide for implementing the framework, demonstrated using two diverse datasets: one from the field of planetary science and another from financial security. The results underscore the effectiveness of the proposed framework in achieving high accuracy and precision when detecting events in both datasets. In summary, this paper presents a novel and robust framework for event detection in time series data, founded on universal approximation theory and bolstered by ensemble learning

The active plasma sheet: definition of 'events' and statistical analysis

International audienceA statistical analysis of the plasma sheet activity is performed from CLUSTER observations (years 2001 to 2004). Different types of 'events' are defined by using the plasma flow velocity (V-events), the low frequency magnetic fluctuations (B-events), and the spectral density of higher frequency waves (HF-events). They are selected by an automatic procedure from 2 criteria: a lower threshold for the fluctuations and a minimal duration for each events. The V-events correspond to the usual 'BBF'. The three types of 'events' form an homogenous set, their number (20 to 50 for each Cluster 'tail' season, depending on the selection criteria) and their total duration (5-10% of the time spent by CLUSTER in the sheet) being comparable. 'Events' of different types are positively correlated with percentages of common detection reaching 50%. They are also organized in bunches that characterize local active states in the plasma sheet. However, these active states do not present a one-to-one relationship with substorms or auroral activations. Analysing how the number of 'events' varies with the selection criteria, it is concluded that the B-events saturate at 2-4 nT and have a rather long duration (more than 1-2 minutes) when HF-events are more likely bursty and intense since their number significantly increases for duration smaller than 1 minute. In average, B-events and HF-events begin before V-events. We cannot conclude on a cause-to-effect relationship between 'events', nevertheless, the study shows that the three types of 'events' are likely related to the same basic physical phenomena. They could be fundamental elements of the plasma sheet turbulence