Time Series Analysis and Prediction of AirPassenger Data using ARIMA Model

Name
Course
Date
Institution

Contents
• Introduction
............................................................................. 3
• Descriptive Statistics
............................................................... 4
• Plots .......................................................
................................... 5
• ARIMA Model .............................
............................................ 7
• Prediction
................................................................................. 9
• Conclusion and Recommendations 2

Introduction
• Several statistical models are used for the prediction
(forecasting) of future values of data. The models include
regression models, neural networks and time series models. In
cases where the data has a time component, the time series
models are best fit for the prediction of future values.
• Time series have three components: trend component, cyclic
(seasonal) component and the random component (Anderson,
2011).
• This research applied time series analysis on the AirPassenger
dataset available in the R Software. An ARIMA (p, d, q) model was
developed and applied with predictions made based on the
resultant ARIMA model. Autocorrelation and Partial
autocorrelation graphs are used to determine the values of p and
q in the model (Zhu and Wang, 2010; Getis, 2010). 3

Descriptive Statistics
• The describe () function in the psych R package was
used for the descriptive statistics. The results of the
descriptive statistics are given in table below:
4
Statistic Value
Mean 280
Minimum 104
Maximum 622
Range 518
Standard Deviation 120
Kurtosis 0.57