PREV

• 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