EPSRC Reference: 
EP/L014246/1 
Title: 
New challenges in time series analysis 
Principal Investigator: 
Fryzlewicz, Professor PZ 
Other Investigators: 

Researcher CoInvestigators: 

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: 

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 highdimensional (e.g. in
macroeconomic modelling, where many potential predictors are frequently included
in models e.g. for GDP growth), nonnormallydistributed (e.g. in finance where daily
returns on many financial instruments show deviations from normality) and
nonstationary, which means that their statistical properties such as the mean,
variance or autocovariance change through time (e.g. in finance where codependence
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 nonstationary,
highdimensional and curvevalued 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) redefine the way in which people think of nonstationarity. We will define
(non)stationarity to be a problemdependent, 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, problemdependent dimensionality reduction procedures for time
series which are both highdimensional and nonstationary (dimensionality reduction
is useful in practice as lowdimensional time series are much easier to handle). We
hope that this problemdependent approach will induce a completely new way of
thinking of highdimensional time series data and highdimensional data in general;
(iii) propose new methods for statistical model selection in highdimensional time series
regression problems, including the nonstationary 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 highdimensional 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 nonstationary 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
analysis.

Key Findings 
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Potential use in nonacademic contexts 
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Impacts 
Description 
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Summary 

Date Materialised 


Sectors submitted by the Researcher 
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Project URL: 

Further Information: 

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