The usefulness of time-series data or cross-sectional data in data science depends largely on the objective of the research. For example – analyzing the effectiveness of marketing campaigns does 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 analyzed and the probability of a prospect getting converted to the customer is devised. Also, it's heavily used in Financial Applications such as predicting the Future Stock prices based on the historical data.
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