H2020Индивидуална стипендия2019–2021

BigTime · Big Time Series Analytics for Complex Economic Decisions

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2019-05-01 → 2021-04-30
Финансиране от ЕС: 175 572 €
Участници: 1
Схема: MSCA-IF-EF-ST

Линиите свързват координатора с партньорите.

Накратко на български

Статистическите методи за анализ на големи масиви от данни с времеви компоненти, като например трафика по пътищата за проследяване на търговията, се развиват тук. Това помага на правителства и компании да вземат по-сигурни и бързи икономически решения.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Big Time Series Analytics for Complex Economic Decisions

Large, complex datasets (‘Big Data’) are nowadays omnipresent in business and economics. Their variety and sheer size provide nearly endless opportunities to improve economic decision making at European governments, companies and universities: social media and internet search data could shed light on consumer sentiment, payment transactions could be informative for private consumption, or road traffic data might reflect trade developments. The aim of this action is to make new statistical methods available for more confident economic decision making through statistical learning from big data. Special emphasis is placed on developing methods for analyzing big time series data as many real-life problems have a time component. While the Big Data concept has been around for years, most organizations now understand they can get significant value through better and/or faster decision making from Big Data. To this end, it is essential that the European Research Area has the relevant expertise from its statistical community to deliver new Big Data technology. Over the recent years, there has been a surge in novel statistical learning methods to analyze large data sets. Yet, analyzing large economic data often pose additional challenges in that many real-life economic problems have a time component that needs to be appropriately addressed. This action treats time dependence not as a nuisance but rather a valuable source of additional information that is leveraged in the statistical analysis. The action’s main objective consists of tackling econometric challenges of developing statistical learning-based Big Time Series Analytics for economic data. It builds a partnership between statistics, machine learning and econometrics. In doing so, it contributes to the society’s ability to enhance economic discovery from Big Time Series Data by equipping researchers, business analysts, policy holders and students with a Big Time Series software toolbox.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Big time series data are commonplace in economics. Their variety and sheer size provide nearly endless opportunities to improve economic decision making at European governments, companies and universities: amongst others, internet search data could shed light on consumer sentiment, social media provide opportunities for improving economic policy analysis, and high-frequency volatility data could be informative for financial risk analysis. While the expansion of these Big Data sources bring possibilities, it also raises ever-increasing statistical challenges since novel methods (for instance, 'penalized' methods) are needed to estimate high-dimensional models containing many parameters. The development of such methods has flourished in the statistical learning community, but they are not geared towards the specificities of economic time series. Econometric time series models typically differ from traditional statistical models in that they require (i) an accurate assessment of the certainty of the economic findings and predictions, (ii) a description of how the economy responds, over time, to exogenous shocks, and (iii) an identification strategy that maps the observed data to the relevant economic parameters of interest. The proposal builds a partnership between econometrics, statistics and machine learning with the aim of addressing these three econometric objectives. It develops statistical learning methods for (i) honest uncertainty quantification (inference), (ii) interpretable economic impulse response functions analysis and (iii) identification of high-dimensional time series models. The suitability of the developed Big Time Series methods is demonstrated for economic applications including financial risk analysis and macro-economic policy analysis. As such, the proposal provides a Big Time Series Analytics toolbox to modern empirical economists that aims to support and improve economic decision making in big, dynamic and complex time series problems.

Оригинален текст от CORDIS (на английски).

Участници

UNIVERSITEIT MAASTRICHT · MaastrichtКоординаторНидерландия