Time Series Analysis with Minitab
Seminarübersicht
| ID | 2203006 |
| Dauer | 2.0 Tage |
| Methoden | Lecture with examples and exercises. |
| Vorwissen | General Knowledge of Mathematics |
| Zielgruppe | Data Analysts |
| Vorgängerkurs | 1203001 |
Ziele
Describe time series
Describe the internal structure of time series
Smoothing and Interpolating Time Series
Smooth time series and use smoothing for Forecasts
Building Deterministic Models
Use Regression Analysis to Build a Time Series Model
Creating ARIMA Models
Use Autoregressive Models for Complex Time Series Analysis
Analyzing Time Series with Minitab
Learn the Minitab Statistical Functions for Time Series
Übersicht
A time series is a time-dependent sequence of data points. Typical examples of time series are macroeconomic variables, market-related data, and technical measurement data. Time series analysis deals with the mathematical-statistical analysis of time series and the prediction of their future development. It is a specialized form of regression analysis. The Minitab Time Series Analysis seminar demonstrates A selection of methods for conducting time series analyses. In the first part, you will learn how to describe a time series and summarize it in key parameters. The second part introduces univariate time series analysis. This includes the decomposition of a time series and the derivation of (autoregressive) regression models using AR, MA, and AR(I)MA models.
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Themen
- Describe and present time series in a structured manner in Minitab
- Build simple models using smoothing and interpolation
- Develop deterministic models for explanation and forecasting
- Determine and use ARIMA models
Beschreibung
Describe time series using descriptive statistics, decompose time series into their components, forecast future values, and model time series using the ARIMA technique.
Services
- Mittagessen / Catering
- Hilfe bei Hotel / Anreise
- Comelio-Zertifikat
- Flexibel: Bis einen Tag vorher kostenlos stornieren
Comelio Medien
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Inhalt
Univariate description of time series
Minitab Functions for Time Series Analysis - Minitab Chart for time series - Autocorrelation: Lag operator, creation and interpretation of the correlogram - Smoothing of time series: Moving averages, exponential smoothing, Holt-Winters method - First- and second-order differences
Decomposition of time series using deterministic models
Component models: additive and multiplicative - Seasonal structures in time series: trend, seasonal adjustment, and derivation of the seasonal figure, forecasting and residual analysis - Level change - Linear, parabolic, logistic, exponential fitting and regression of time series - Polynomials - Goodness of fit
Minitab Macros for Time Series
Periodogram: derivation and interpretation, to analyze periodic oscillations – cross-correlation in multiple time series
Univariate Linear Time Series Models with AR(I)MA
Stationarity in Time Series – White Noise Processes - AR (Autoregressive) Models - MA (Moving Average) Models - ARMA and ARIMA Models – Forecasting - Residual Analysis – Statistical Tests for Linear Time Series Models – Goodness of Fit and Model Selection - Seasonal Components with SARIMA
Dozent/in
Our statistics trainer for DOE (Design of Experiments) and SPC (Statistical Process Control) and data analysis with Minitab Marco Skulschus studied economics in Wuppertal and Paris and has been working for more than 10 years as a lecturer, author of specialist books on databases and data analysis, and as a consultant for statistical analysis with Minitab.
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