Project description
Understanding and predicting time series data
Time series characterise diverse systems that can range from proton motion to the Dollar/Yen exchange rate. To understand, compare, classify and forecast time series data, people commonly use stochastic differential equations, diverse random walk models and machine-learning algorithms but these leave fundamental questions unanswered. To overcome this problem, the EU-funded NoMaMemo project aims to create a generic platform to analyse, understand, compare, classify and predict time series data and to optimise stochastic systems. It will provide a unified description of generic time series data in terms of non-linear integro-differential stochastic equations based on memory functions extracted from data. Through its approach, the project will significantly advance the understanding of multiple scientific systems and processes.
Objective
Time series characterize diverse systems, examples in this proposal are: i) Proton motion in an inhomogeneous aqueous environment, ii) folding and unfolding of a peptide described by a suitably chosen reaction coordinate, iii) migration of a living cell on a substrate, iv) US Dollar / Yen exchange rate. Examples i) and ii) are close-to-equilibrium, iii) is a far from equilibrium since energy is constantly dissipated, while example iv) at first sight defies the classification into equilibrium or non-equilibrium.
For the understanding, comparison, classification and forecasting of time series data, stochastic differential equations, diverse random walk models, and more recently, machine-learning algorithms are commonly used. But fundamental questions remain unanswered: Is a unified description of such diverse systems possible? What is the relation between different proposed models? Can the non-equilibrium degree of a time series be estimated?
NoMaMemo provides a unified description of generic time series data in terms of non-linear integro-differential stochastic equations based on memory functions that are extracted from data. NoMaMemo accounts for non-linear and non-equilibrium effects as well as for non-Gaussian noise and connects with fundamental concepts such as equilibrium statistical mechanics, response theory and entropy production. The general formulation contains previously proposed models and thus allows their comparison, forecasting quality will be compared with modern machine-learning algorithms. NoMaMemo creates a generic platform to analyse, understand, compare, classify and predict time series data and to optimize stochastic systems with respect to search efficiency, barrier-crossing speed or other figures of merit. NoMaMemo will significantly advance the understanding of chemical reaction and protein folding kinetics, the interpretation of THz and IR spectroscopy of liquids and the analysis of living matter and socio-economic data.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences biological sciences biochemistry biomolecules proteins
- natural sciences physical sciences optics spectroscopy
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
ERC-ADG - Advanced Grant
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2018-ADG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
14195 BERLIN
Germany
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.