Time series data are ubiquitous. The broad diffusion and adoption of Internet of Things, and major advances in sensor technology are examples of why such data have become pervasive. These technologies have applications in several domains, such as healthcare, finance, meteorology and transportation, for solving related tasks on time series. Deep Neural Networks have recently been used to create models that improve on the state of the art for some of these tasks. In some scenarios obtaining a training set that matches the feature space and predicted data distribution characteristics of the test set can be time consuming, difficult and expensive. Thus, there is a need for focusing on modern AI techniques that can extract value from small and irregular data. These considerations can also contribute to conform with the increasing need to address the sustainability and privacy aspects of ML and AI. The goal of this project is to overcome the issue of limited available or labelled data in the time series domain, where the heterogeneity of the data, e.g. non-stationarity, multi-resolution, irregular sampling, poses a further challenge. ML4ITS's main objective is to advance the state-of-the-art in time series analysis for 'irregular' time series data (see explicit definition of 'irregular time series' in the proposal). We plan to achieve these goals and tackling the 'robustness' challenge, by developing novel A) Transfer Learning and B) Unsupervised learning and Data Augmentation methods. These techniques have hardly been explored in the time series domain. We relies on a multidisciplinary study combining different perspectives from the three main scientific communities involved in time series analysis. As a result, the consortium has been composed with this complementarity in mind including researchers across these different fields (IES, MATH, IDI).
Project leader: Massimiliano Ruocco
Institution: Institutt for datateknologi og informatikk