Components of time series data, Trend, Secular trend and there difference, Seasonal Variation Cyclical Fluctuation, Irregular Variations
A time series is a collection of observations of well-defined data items obtained through repeated measurements over time. For example, measuring the value of retail sales each month of the year would comprise a time series. This is because sales revenue is well defined, and consistently measured at equally spaced intervals. Data collected irregularly or only once are not time series.
An observed time series can be decomposed into three components: the trend (long term direction), the seasonal (systematic, calendar related movements) and the irregular (unsystematic, short term fluctuations)
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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
