Simple Steps for Fitting Arima Model to Time Series Data for Forecasting Using R

Time series deals with data that has been recorded or observed over time. These data may need to be analyzed to come up with conclusions and meet the objectives intended by the researcher. A time series may be expressed as an additive model of its components which includes the seasonal, the cyclic, the trend and irregular components. When time series data is analyzed it becomes very key in forecasting or prediction of future time series values, in control of machines among others. In this study it has been noted that though most researchers may be in a position to collect time series data, it is a challenge in analyzing it since some of the steps they are aware of may be complex and not straight forward. This then implies that analysis of time series data needs a great understanding and knowledge of the procedure and the models that can be useful in meeting the researcher