Write a reply for 'One application of time series data mining I found is that scholars have analyzed time series data of 50 marathon runners' physical metrics, including speed, heart rate, acceleration, stride frequency, and stride length (Ding, 2024). The research revealed that estimating performance trends can be beneficial in optimizing athletes' training menus. For example, abnormal performance far from prediction might indicate overtraining or injuries. Another application was the decision table transformation method on time series financial data, such as stock technical factors with trend turning points and historical extreme points (Yang et al., 2022). The study proposed that different time granularity and a trend division method can determine the rise or fall modes of the stock time series (Yang et al., 2022). It also exhibited that data characteristics still exist in time series mining, avoiding a common issue in stock data research. Both examples elaborate on how leveraging time series data enhances prediction and business decision-making. Furthermore, I also observed that transaction data can help strategists design and test marketing campaigns more accurately in my internship