Hi Krupa,

Thanks for sharing your question here.

There are two articles I’d like to suggest. The first one: “How To Identify Patterns in Time Series Data: Time Series Analysis”. Available on: http://www.statsoft.com/textbook/time-series-analysis.

From there I’d like to highlight this statement:

“Most time series patterns can be described in terms of two basic classes of components: trend and seasonality. The former represents a general systematic linear or (most often) nonlinear component that changes over time and does not repeat or at least does not repeat within the time range captured by our data (e.g., a plateau followed by a period of exponential growth). The latter may have a formally similar nature (e.g., a plateau followed by a period of exponential growth), however, it repeats itself in systematic intervals over time. Those two general classes of time series components may coexist in real-life data. For example, sales of a company can rapidly grow over years but they still follow consistent seasonal patterns (e.g., as much as 25% of yearly sales each year are made in December, whereas only 4% in August).”

The second article: “How to Identify and Remove Seasonality from Time Series Data with Python”. Available on: https://machinelearningmastery.com/time-series-seasonality-with-python/

It brings us a more practical point of view.

I hope can help you in somehow :)