tsampling

tsampling is a technique used in time series analysis for selecting a subset of observations from a longer time series. This selection is done at regular intervals, effectively reducing the number of data points while retaining the temporal structure. The primary goal of tsampling is to simplify data for analysis, reduce computational costs, or to focus on specific patterns that might be obscured by high-frequency data.

The effectiveness of tsampling depends on the sampling rate chosen. A higher sampling rate means more data points are kept, potentially preserving more detail but offering less simplification. Conversely, a lower sampling rate drastically reduces the data size but risks losing important short-term fluctuations or cyclical patterns. The choice of sampling rate is often guided by the specific characteristics of the time series and the objectives of the analysis.

Common applications of tsampling include downsampling historical data for more efficient storage and processing, preparing data for machine learning models that may have limitations on input size, or creating visual representations of long time series where observing every single data point would be impractical. It is a form of data aggregation that can be useful in various fields, from finance and economics to sensor data processing and environmental monitoring. Care must be taken to ensure that the tsampling process does not introduce significant bias or distort the underlying temporal dynamics of the original series.