Introduction to Time Series, time series data and it's different types
A time series data is a sequence of data points, which is collected over consecutive time interval. Analysis of the time series includes methods for assessing information from time series to gain significant statistics and other information features. Time series mostly plot on the line chart. Time series data can be collected hourly, daily, weekly, monthly, quarterly and yearly. Different types of time series are available which include physical time series, marketing time series, economic and financial, demographic, Process control data, the point process, Binary process, Continuous and discrete time series.
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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
