Mini-course: Advanced Stationary Processes Analysis (Time Series Analysis). VIASM: 14/7 – 22/7/2016

Mini-course: Advanced Stationary Processes Analysis (Time Series Analysis). VIASM: 14/7 – 22/7/2016

Thời gian: 09:00:14/07/2016 đến 17:00:22/07/2016

Địa điểm: B4-705

Báo cáo viên: Jean-Yves Dauxois, INSA-IMT University of Toulouse, France; Vincent Lefieux, RTE Paris, France.

Download Lecture:

1. Mini-course-Dauxois.pdf.

2. Mini-course-Dauxois-2.pdf

3. TP_Geostat_Rmd.pdf

Lecture V. Lefieux1

Lecture V. Lefieux2

Lecture V. Lefieux3

Lecture V. Lefieux 4

Lecture V. Lefieux 5

Lecture V. Lefieux 6

R_Lefieux1.pdf

Tóm tắt:

The time series analysis is a very well known part of Statistics and used in many areas like: economics, finance, environment, energy, medicine, geophysics… The analysis of such stochastic processes is strongly based on the property of stationarity. The aim of this lecture is to introduce some extensions of this models and to study their statistical inference.

One part of the lecture will be devoted to multivariate time series and an introduction of spatial time series. In dealing with economic time series, we often need to forecast stochastic processes based non only on their own past values but also on other processes. In order to achieve this, we can use VAR models, cointegration theory and state space models which constitute a nice statistical framework to address common challenges.

Another part of the lectures will consider the field of Geostatistics which aim is to make statistical inference on some kinds of Spatial processes. Assuming a weaker notion of Stationarity (Intrinsic property), one can estimate a variable of interest over a whole domain (seen as a realization of a spatial process) on the basis of the observation on a limited number of points. The Kriging is the most used technique in this area. Applications are often encountered in hydrology, meteorology, oceanography, geography, among others…

Lectures will be accompanied by tutorials and computer lab works with R software.

Schedule.

Thusday 14th Friday 15th
Basic concepts of Time Series Analynis

(stationarity, ARMA models and forecasting)

9am to 12am 9am to 12am
2pm to 5pm 2pm to 5pm
Monday 18th Tuesday 19th Wednesday 20th Thusday 21th Friday 22th
Adv. Time Series An. 9am to 12am 2pm to 5pm 9am to 12am 2pm to 5pm 9am to 12am
Geostatistics 2pm to 5pm 9am to 12am 2pm to 5pm 9am to 12am 2pm to 5pm

Program.

Advanced Time Series Analysis (Vincent Lefieux):

Basic concepts of Time Series Analynis (stationarity, ARMA models and forecasting)

– Reminders on stationarity and ARMA models.

– Multivariate Time Series Analysis, including: VAR models, Cointegration, State-Space Model.

– Introduction to Spatial Time Series.

Geostatistics (Jean-Yves Dauxois):

– Spatial processes, Stationarity, Intrinsic Processes, Variogram, Models of Variogram, Statistical Inference.

– Kriging: Simple Kriging, Ordinary Kriging and Universal Kriging.

Prerequisites.

Probability.

Linear Models.

Inferential Statistics.

Basic concepts in Time Series Analysis.

Basic concepts in R software.

References.

Advanced Time Series Analysis:

– Hamilton, J.D. (1994). Time series analysis. Princeton University Press.

– Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer.

– Tsay, R.S. (2014). Multivariate time series analysis with R and financial applications. Wiley.

Geostatistics :

– Chilès, J.P. and P. Delfiner (1999). Geostatistics. Wiley, New York.

– Diggle, P.J. and Ribeiro, P.J. (2007). Model-based Geostatistics. Springer.

– Guyon, X. (1995). Random Field on a network. Springer, New York.

Deadline for registration: July 11, 2016. http://viasm.edu.vn/hdkh/mini-course-advanced-stationary-processes-analysis-time-series-analysis?userkey=dang-ky-tham-du

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