Moving Average models its mean, variance, autocovariance, autocorrelation function.
The MA model is abstractly a linear regression of the current values of the series beside previous white noise error terms or random tremors.
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Course Material
- Introduction to Time Series, time series data and it's different types
- Components of time series data, Trend, Secular trend and there difference, Seasonal Variation Cyclical Fluctuation, Irregular Variations
- Decomposition of time Series Data Recomposition of time Series Data
- Exponential smoothing technique, simple exponential smoothing,
- Holts corrected trend exponential smoothing, Holt’s winter exponential smoothing, Zaitun Software
- Transformation of Data and its type, Box-Cox transformation Stationary Process, Strict stationary, Weak stationary, Trend stationary, Difference stationary.
- How to Handle real life Data, Missing values, trend analysis, seasonal analysis, Outliers, its detection, techniques and Stochastic Process.
- Autocovariance function, Autocorrelation function, Partial Autocorrelation function, Acf, PACF, Correlogram. Properties of Autocorrelation function
- Periodogram, spectral density functions, comparison with ACF, Linear stationary models.
- Random Process, mean , variance, autocovariance, autocorrelation function. Random Walk model its mean, variance, autocovariance, autocorrelation function.
- Autoregressive models its mean, variance, autocovariance, autocorrelation function.
- Moving Average models its mean, variance, autocovariance, autocorrelation function.
- Mixed models, Autoregressive Moving Average models (ARMA models) its mean, variance, autocovariance, autocorrelation function.
- Non-stationary models, General ARIMA notation and models its mean, variance, autocovariance, autocorrelation function.
- Model Selection, Box and Jenkins Methodology, Forecasting.
- Practical work of Linear trend,AR, MA, ARIMA, SARIMA, ANN model on differnt Software
- Chapters 16
- Department Statistics
- Teacher
Muhammad Wasim Amir
