Statistics - Time Series Analysis
Details
| ID | 2858612 |
| Duration | 2.0 days |
| Methods | Lecture with examples and exercises. |
| Prerequisites | General knowledge of math |
| Target group | Data Analysts |
Overview
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. The course provides tools for empirical work with time series data and is an introduction into the foundation of time series models. It focuses on both univariate and multivariate time series analysis. After completing this course, a student will be able to analyze univariate and multivariate time series data using available software like MS Excel, SPSS and jMulti.
Dates
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Services
- Lunch / catering
- Help with hotel / travel
- Comelio certificate
- Flexible: free cancellation up to one day before
Content
Univariate analysis of time series data
Estimation of the moment-generating functions (expected value, auto-covariance) - auto-correlation: the lag operator, creating and interpretating the correlogram - smoothing of time series data: moving averages, exponential smoothing - transformation and filtering of time series data - first-order and second-order differencesDecomposition of time series using deterministic models
Component models: additive and multiplicative models - seasonal structures in time series: trend, seasons and identification of the seasonal pattern, prognosis and residual analysis - level shifts - linear, parabolic, logistic, exponential fit and regression of time series - polynomials - quality measuresPeriodicities in time series
Trigonometric functions and their importance for periodic trends - period detection and frequencies - periodogram: identification and interpretation - regression models with periodic oscillations - spectra and spectral density estimation of time series - introduction to Fourier transformation for time seriesUnivariate linear time series models using AR(I)MA
Stationarity in time series - White Noise process - AR (Auto Regressive)-models - MA (Moving Average)-models - ARMA and ARIMA models - forecasting - residual analysis - statistical tests for linear time series models - quality measures and model selectionAnalysis of multidimensional time series
Cross-correlation and cross-covariance - stationary cross-covariance - co-integration - introduction to cross-spectral analysis and coherence analysisMultidimensional time series using VAR
VAR (Vector AutoRegressive) processes: modeling, prediction, residual analysis, quality measures, testsTime series with exogenous influences
Regression with auto-correlated shocks - intervention analysis - transfer function modelsInstructor
Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.Publications
- Grundlagen empirische Sozialforschung (Comelio Medien)
978-3-939701-23-1 - System und Systematik von Fragebögen (Comelio Medien)
978-3-939701-26-2 - Oracle PL/SQL (Comelio Medien)
978-3-939701-40-8 - MS SQL Server - T-SQL Programmierung und Abfragen (Comelio Medien)
978-3-939701-69-9