Resampling time series data
A typical transformation that is done on time series data is resampling. The process implies changing the frequency or level of granularity of the data.
Usually, you will have limited control over how the time series is generated in terms of frequency. For example, the data can be generated and stored in small intervals, such as milliseconds, minutes, or hours. In some cases, the data can be in larger intervals, such as daily, weekly, or monthly.
The need for resampling time series can be driven by the nature of your analysis and at what granular level you need your data to be. For instance, you can have daily data, but your analysis requires the data to be weekly, and thus you will need to resample. This process is known as downsampling. When you are downsampling, you will need to provide some level of aggregation, such as mean, sum, min, or max, to name a few. On the other hand, some situations require you to resample your data from daily to hourly...
