TIME SERIES ANALYSIS A time series is a set of data collected or organized in a sequence of order over a successive equal increment of time (Lazim MA, 2011). Time series are also described as a sequence of data within a uniform time interval in terms of years, months, days, hours, and so on. Why am I interested in time series analysis? Time series analysis is a quantitative or statistical technique used to predict certain set of data over a period of time and can be evaluated for accuracy by applying certain measurement criteria. The application of time series data involves the formulation of mathematical models, which are estimated using a significantly large amount of data to mimic the environment under study and thus generate the forecast value. Time series analysis and its applications have become increasingly important in diverse research fields, such as business, economics, engineering, medical, environmental, social sciences, politics, and others. It is interesting to forecast or in other words predict data over a given time interval to provide information for decision-making activities, especially in the area of ​​planning and control. A good planning procedure would help deal with the growing uncertainties of the future. For example, if the value (sales, stock prices, etc.) of a certain product will decrease next year, the decision maker may want to plan an action that would help reduce or minimize losses and vice versa. Time series are used by social scientists in population series such as birth rate and death rate. In medicine, blood pressure over time can be used to evaluate medications used to reduce the symptoms of hypertension. Time series analysis is also useful for environmental data, such as tsunami waves, earthquakes, volcanic eruptions, landslides, calving glaciers, meteorite impacts, floods, etc. to predict the potential occurrence of natural disasters in the future. Therefore, prevention and control can save human lives and avoid tragedies. It would be an honor if I could do it. Time series analysis would be an advantage to monitor the progress achieved, in order to determine whether the set goal is currently being achieved or can be achieved in the near future. Using historical data over time we will be able to predict and estimate the future if the most suitable method is used. The objectives of time series analysis are to identify and describe the underlying structure and phenomenon as represented by the sequence observations in the series and to determine the best mathematical model to fit the data series and use the model to generate forecast value.