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Details of Grant 

EPSRC Reference: EP/L014246/1
Title: New challenges in time series analysis
Principal Investigator: Fryzlewicz, Professor PZ
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Statistics
Organisation: London School of Economics & Pol Sci
Scheme: EPSRC Fellowship
Starts: 01 April 2014 Ends: 31 March 2019 Value (£): 1,044,886
EPSRC Research Topic Classifications:
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
21 Jan 2014 EPSRC Mathematics Interviews - January 2014 Announced
27 Nov 2013 Mathematics Prioritisation Panel Meeting Nov 2013 Deferred
Summary on Grant Application Form
Time series are observations on a quantity or quantities, collected through time.

They arise in many important areas of human endeavour, for example

finance (daily closing values of the FTSE 100 index; the order book of a financial

instrument evolving over time), economics (interest rates set by a central bank;

monthly changes to macroeconomic indicators; yield curves changing through time),

engineering (speech signals), natural sciences (temperature, seismic signals) and

neuroscience (brain activity measurements via EEG, fMRI or other techniques), to

name but a few. Typical tasks faced by time series analysts include understanding

the nature of and modelling the evolution of the time series, forecasting its future

values, understanding how it impacts and is impacted by other factors, and

classifying it to one of a number of categories. Solving these tasks adequately

can have enormous positive impact on economy and society.

Modern time series datasets often defy traditional statistical assumptions. In many

contexts, time series data are massive in size and high-dimensional (e.g. in

macroeconomic modelling, where many potential predictors are frequently included

in models e.g. for GDP growth), non-normally-distributed (e.g. in finance where daily

returns on many financial instruments show deviations from normality) and

non-stationary, which means that their statistical properties such as the mean,

variance or autocovariance change through time (e.g. in finance where co-dependence

structure of markets is known to change in times of financial crises). Often, time series

data arise as complex objects such as curves (e.g. yield curves). New theories and

methods are needed to handle these new settings.

The proposed research will break new ground in the analysis of non-stationary,

high-dimensional and curve-valued time series. Although many of the problems we

propose to tackle are motivated by financial applications, our solutions will be

transferable to other fields. In particular, we will

(i) re-define the way in which people think of non-stationarity. We will define

(non-)stationarity to be a problem-dependent, rather than `fixed' property of time

series, and propose new statistical model selection procedures in accordance with

this new point of view. This will lead to the concept of (non-)stationarity being

put to much better use in solving practical problems (such as forecasting) than

it so far has been;

(ii) propose new, problem-dependent dimensionality reduction procedures for time

series which are both high-dimensional and non-stationary (dimensionality reduction

is useful in practice as low-dimensional time series are much easier to handle). We

hope that this problem-dependent approach will induce a completely new way of

thinking of high-dimensional time series data and high-dimensional data in general;

(iii) propose new methods for statistical model selection in high-dimensional time series

regression problems, including the non-stationary setting. Our new methods will be

useful in fields such as financial forecasting or statistical market research;

(iv) investigate new methods for statistical model selection in high-dimensional time

series (of, e.g., financial returns) in which the dependence structure changes in an

abrupt fashion due to `shocks', e.g. macroeconomic announcements;

(v) propose new multiscale time series models, specifically designed to solve a long-

standing problem in finance of consistent modelling of financial returns on multiple

time scales, e.g. intraday and interday;

(vi) propose new ways of analysing time series of curves (e.g. yield curves) which

can be non-stationary in a variety of ways.

Overall, this is a comprehensive and ambitious research programme, which aims to

offer novel solutions to some of the most important questions in modern time series


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Organisation Website: http://www.lse.ac.uk