Events

Ai - AT - Analysis of time series

at 13 KM
Elgon Follow

Training
Software
Standard
Statistics
IT

mon. 21 Sep.

tue. 22 Sep.

mon. 26 Oct.

tue. 27 Oct.

mon. 23 Nov.

tue. 24 Nov.

tue. 1 Dec.

wed. 2 Dec.

mon. 14 Dec.

tue. 15 Dec.

Used in different fields such as industry, finance, trade, TEMPORAL series allow the development of models of predictions based on the observation of past events. Statistical analysis of this type of series requires an approach and a particular method which will be presented to you during this training through different practical cases.
INTRODUCTION TO THE TEMPORAL SERIES Presentation of the TEMPORAL series. Graphic for the TEMPORAL series (chronogram, lag plot, month plot). Trends, seasonality and residuals. Steps and objectives of the analysis of a time series. Linear regression. BASIC MODEL IN TEMPORAL SERIES stationarity Linear series Autocorrelation function Prediction and estimation Construction of an ARMA or SARMA model TEMPORAL NON STATIONARY SERIES ARIMA or SARIMA models Non stochastic or deterministic non-stationarity EXPONENTIAL SMOOTH Simple exponential smoothing Double exponential smoothing Holt-Winters.SIMULATIONSimulation of TEMPORAL series.construction of autoregressive series.Construction of series with intervention.Practical cases are performed using Python language, but can also be performed IN r language
The prerequisite for this training is knowledge of Python or r languages.

Good to know

- 2-day training in French
- Price upon request
- The necessary prerequisite for this training consists of knowledge of Python or R languages

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