Talk of Andrey Shternshis (Scuola Normale Superiore di Pisa) on "Forecasting quantiles with reservoir computers and time-varying regimes of entropy"
11:29 a.m.
Dear all,
On 5 April at 14:30, Andrey Shternshis will give a talk in room C2.09.
Title
Forecasting quantiles with reservoir computers and time-varying regimes of entropy
Abstract
I present a machine learning approach for predicting quantiles of time series. Reservoir
computers are designed with the quantile loss function to forecast quantiles associated
with different probability levels. I compare Reservoir computing approach with a known
statistical approach proposed by Engle and Manganelli (CAViaR, 2004). First, I do a
comparison analysis for data simulated by a random logistic map. Then, I compare the
mentioned methods on financial time series. I show that the machine learning approach
based on recurrent neural networks outperforms the statistical approach especially when a
signal-noise ratio is relatively high.
In the second part of the presentation, I will give some results related to my thesis
topic. Real-world systems may be in general non stationary, with an entropy value that is
not constant in time. We propose a hypothesis testing procedure to test the null
hypothesis of constant Shannon entropy for time series, against the alternative of a
significant variation of the entropy between two subsequent periods. We find the optimal
length of the rolling window used for estimating the time-varying Shannon entropy by
optimizing a novel criterion.
Speaker
Andrey Shternshis is a PhD student at Scuola Normale Superiore of Pisa in the field of
Computational Methods and Mathematical Models for Sciences and Finance.
Best wishes,
Laura
–––––––––––––––––––––––––––––––
http://people.idsia.ch/~laura.azzimonti/
10:03 p.m.
New subject: Cancelled: Talk of Andrey Shternshis (Scuola Normale Superiore di Pisa) on "Forecasting quantiles with reservoir computers and time-varying regimes of entropy"
Dear all,
Unfortunately, the talk scheduled for tomorrow (5 April) is cancelled.
Best wishes,
Laura
–––––––––––––––––––––––––––––––
http://people.idsia.ch/~laura.azzimonti/
On 3 Apr 2023, at 11:29, IDSIA Announcements of talks at IDSIA
<talks(a)lists.idsia.ch> wrote:
Dear all,
On 5 April at 14:30, Andrey Shternshis will give a talk in room C2.09.
Title
Forecasting quantiles with reservoir computers and time-varying regimes of entropy
Abstract
I present a machine learning approach for predicting quantiles of time series. Reservoir
computers are designed with the quantile loss function to forecast quantiles associated
with different probability levels. I compare Reservoir computing approach with a known
statistical approach proposed by Engle and Manganelli (CAViaR, 2004). First, I do a
comparison analysis for data simulated by a random logistic map. Then, I compare the
mentioned methods on financial time series. I show that the machine learning approach
based on recurrent neural networks outperforms the statistical approach especially when a
signal-noise ratio is relatively high.
In the second part of the presentation, I will give some results related to my thesis
topic. Real-world systems may be in general non stationary, with an entropy value that is
not constant in time. We propose a hypothesis testing procedure to test the null
hypothesis of constant Shannon entropy for time series, against the alternative of a
significant variation of the entropy between two subsequent periods. We find the optimal
length of the rolling window used for estimating the time-varying Shannon entropy by
optimizing a novel criterion.
Speaker
Andrey Shternshis is a PhD student at Scuola Normale Superiore of Pisa in the field of
Computational Methods and Mathematical Models for Sciences and Finance.
Best wishes,
Laura
–––––––––––––––––––––––––––––––
http://people.idsia.ch/~laura.azzimonti/
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