| EPSRC Reference: |
EP/L014246/1 |
| Title: |
New challenges in time series analysis |
| Principal Investigator: |
Fryzlewicz, Professor PZ |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Statistics |
| Organisation: |
London School of Economics & Pol Sci |
| Scheme: |
EPSRC Fellowship |
| Starts: |
01 April 2014 |
Ends: |
31 March 2019 |
Value (£): |
1,044,886
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| EPSRC Research Topic Classifications: |
| Statistics & Appl. Probability |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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| Related Grants: |
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| Panel History: |
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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
analysis.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.lse.ac.uk |