The description of the objectives of time series analysis are as follows:
The first step in the analysis is to plot the data and obtain simple descriptive measures (such as plotting data, looking for trends, seasonal fluctuations and so on) of the main properties of the series. In above figure , there is a regular seasonal pattern of price change although this price pattern is not consistent. Graph enables to look for “wild” observations or outlier (not appear to be consistent with the rest of the data). Graphing the time series make possible the presence of turning points where upward trend suddenly changed to a downward trend. If there is turning point, different models may have to be fitted to the two parts of the series.
Observations taken on two or more variables, making possible to use the variation in one time series to explain the variation in another series. This may lead to deeper understanding. Multiple regression model may be helpful in this case.
Given an observed time series, one may want to predict the future values of the series. It is an important task in sales of forecasting and is the analysis of economic and industrial time series. Prediction and forecasting used interchangeably.
When time series generated to measure the quality of a manufacturing process (the aim may be) to control the process. Control procedures are of several different kinds. In quality control, the observations are plotted on control chart and the controller takes action as a result of studying the charts. A stochastic model is fitted to the series. Future values of the series are predicted and then the input process variables are adjusted so as to keep the process on target.