The usefulness of time-series data or cross-sectional data in data science depends largely on the objective of the research. For example – analysing effectiveness of marketing campaigns do not require time-series data as it is a one-time affair. However, considering both descriptive as well as predictive analytics on historical data trends & patterns; time series data is extremely important.
A perfect example of the same is in any business wherein a customer journey is tracked and analysed and the probability of a prospect getting converted to customer is devised. Also, its heavily used in Financial Applications such as predicting the Future Stock prices based on the historical data.
The time-series analysis is a very important concept in Data Science.
It is basically done in two domains, frequency-domain and the time-domain.
Both of them play a vital role in intense computational analysis and also optimization science.
One example is the time-series forecasting, in which the output of a particular process can be forecast by analyzing the previous data, by various methods like exponential smoothening, moving averages, log-linear regression method, etc.
Many data scientists have been using this method for various applications and studies.
One premier example can be Prof. Hans Rosling’s analysis of child death-rate in the African countries.
The frequency domain is also used extensively in the bio-informatics field and also in the study of biological evolution, etc.