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TIME SERIES ANALYSIS

Any ordered temporal variable may be regarded as a time series. These variables are used to understand the past behaviour and to predict the future. In this way, they are able to inform the decision-making process, which is important for many aspects of life. However, the obvious correlation introduced by the sampling of adjacent points in time can severely restrict the applicability of the many conventional statistical methods that usually depend on the assumption that these adjacent observations are independent and identically distributed. This has led to the development of a number of innovative solutions that facilitate investigations into the study of time series analysis.

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The dropbox folder contains exercises, research papers and industry cases that will be covered in time series analysis.

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is a coding software for statistical computing. Download here.

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                 is a free, open-source, software. Download here.

               

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I have a blog titled review of different softwre packages. 

Module 0:  An Introduction to Time Series Econometric Modelling

Module 1:  Autoregressive Time Series Models (AR Models)

Module 2:  Moving Average Models, ARMA Models and ARIMA Models

Module 3:  Box Jenkins Methodology

Module 4:  Stationary Process and Non-Stationary Process

Module 5:  Why Does Stationarity Matter

Module 6:  Types of Random Walk Models

Module 7:  Vector Autoregression Model

Module 8:  Error Correction Models

Module 9:  Auto Regressive Distributed Lag Model

Module 10:  Modelling Variance or Volatility of the Time Series Observations

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