Time series analysis is a statistical method to analyse the past data within a given duration of time to forecast the future. It comprises of ordered sequence of data at equally spaced interval.To understand the time series data & the analysis let us consider an example. Consider an example of Airline Passenger data. It has the count of passenger over a period of time.
Increasing or decreasing pattern has been observed over a period of time. In this case, the gradually increasing underlying trend is observed. i.e. the count of passengers has increased over a period of time.
Linear and Non-Linear Trend If we plot the time series values on a graph in accordance with time t. The pattern of the data clustering shows the type of trend. If the set of data cluster more or less round a straight line, then the trend is linear otherwise it is non-linear
Refers to cyclic pattern. A similar pattern that repeats after a certain interval of time. In the airline passenger example, we can observe a cyclic pattern which has a certain high & a low point which is visible in all the interval.
This is also called noise. Irregularity happens for a short duration and it’s non depleting. A very good example is the case of Ebola. During that period, there was a massive demand for hand sanitizers which happened erratically/systematically in a way no one could have predicted, hence one could not tell how much number of sales could have been made or tell the next time there’s going to be another outbreak.
This is when a series is repeating upward and downward movement. It usually does not have a fixed pattern. It could happen in 6months, then two years later, then 4 years, then 1 year later. These kinds of patterns are much harder to predict. When not to apply TS