Adjusting Time-Series Data Frequency - Resample

Imagine a time-lapse video that compresses hours into seconds or stretches moments into minutes, giving you a new perspective on the events. Similarly, in data analysis, we often need to change the time frequency of our data to better understand trends and patterns. This process is called "resampling."

What is Resampling? 

Resampling in time-series data is akin to changing the 'zoom' level on a timeline. It involves adjusting the frequency at which time-series data points are recorded. This can mean increasing the frequency (upsampling) or decreasing it (downsampling).

Why Resample Time-Series Data?

  1. Consistency: Make data from different sources align in time for comparison.
  2. Analysis: Analyze trends over different time intervals.
  3. Data Aggregation: Simplify data by reducing the frequency and summarizing in larger time buckets.
  4. Data Expansion: Increase the frequency for a finer-grained view or to match a desired time interval.

Resampling Techniques:

  1. Downsampling: Reduce data frequency, such as converting from daily to monthly data, often with aggregation like summing or averaging.
  2. Upsampling: Increase data frequency, like going from yearly to quarterly data, which may require interpolation or filling methods to estimate the in-between points.
  3. Interpolation: When upsampling, you might not have data for all the new points, so interpolation can estimate these values based on existing data points.

Example of Resampling

Consider a dataset that tracks the hourly energy usage of a building. To understand daily patterns, you could downsample to daily usage by summing up the 24 hourly records into one daily total. Conversely, if you only have daily data but need to estimate hourly usage, you could upsample, interpolating the values in between.

Resampling on Our Platform: 

Our platform offers a suite of tools designed to resample your time-series data effortlessly. Through a user-friendly interface, you can choose your new frequency and preferred method of interpolation or aggregation. Visual guides and immediate feedback help ensure that the resampling aligns with your analysis goals.

By adjusting the lens through which we view our time-series data, resampling allows us to uncover insights that might be hidden at the wrong scale, much like finding the right focus through a camera's viewfinder.