Forecasting Using SAS(R) Software: A Programming Approach

Kód kurzu: FETS42

Táto časť nie je lokalizovaná

This course teaches analysts how to use SAS/ETS software to diagnose systematic variation in data collected over time, create forecast models to capture the systematic variation, evaluate a given forecast model for goodness of fit and accuracy, and forecast future values using the model. Topics include Box-Jenkins ARIMA models, dynamic regression models, and exponential smoothing models.

Odborní
certifikovaní lektori

Mezinárodne
uznávané certifikácie

Široká ponuka technických
a soft skills kurzov

Skvelý zákaznicky
servis

Prispôsobenie kurzov
presne na mieru

Termíny kurzov

Počiatočný dátum: Na vyžiadanie

Forma: Na vyžiadanie

Dĺžka kurzu: 21 hodín

Jazyk: en

Cena bez DPH: 1 800 EUR

Registrovať

Počiatočný
dátum
Miesto
konania
Forma Dĺžka
kurzu
Jazyk Cena bez DPH
Na vyžiadanie Na vyžiadanie 21 hodín en 1 800 EUR Registrovať
G Garantovaný kurz

Nenašli ste vhodný termín?

Napíšte nám o vypísanie alternatívneho termínu na mieru.

Kontakt

Cieľová skupina

Táto časť nie je lokalizovaná

Scientists, engineers, and business analysts who have the responsibility of forecasting or evaluating policies and practices for their organizations

Štruktúra kurzu

Táto časť nie je lokalizovaná

Introduction to Forecasting

  • Time series and forecasting.
  • Introduction to forecasting with SAS software.
  • Evaluating forecasts.

Stationary Time Series Models

  • Introduction to stationary time series.
  • Automatic model selection techniques for stationary time series.
  • Estimation and forecasting for stationary time series.

Trend Models

  • Introduction to nonstationary time series.
  • Modeling trend.
  • Alternatives to PROC ARIMA for modeling trend.

Seasonal Models

  • Seasonal ARIMA models.
  • Alternatives to PROC ARIMA for fitting seasonal models.
  • Forecasting the airline passengers data.

Models with Explanatory Variables

  • Ordinary regression models.
  • Event models.
  • Time series regression models.

Predpokladané znalosti

Táto časť nie je lokalizovaná

Before attending this course, you should have:
  • Experience using SAS to enter or transfer data and to perform elementary analyses, such as computing row and column totals and averages, and producing charts and plots. You can gain this experience by completing the SAS Programming 1: Essentials course.
  • Experience in data analysis and statistical modeling. You can gain the prerequisite knowledge by completing the Statistics 2: ANOVA and Regression course.
  • Experience with stationary ARMA models and elementary forecast models like time trend models and exponential smoothing models for forecasting. You can gain this experience by completing the Time Series Modeling Essentials course.
  • Potrebujete poradiť alebo upraviť kurz na mieru?

    pruduktová podpora