Recently, I needed to process some time-series data at work and perform downsampling. To do this, I dug into the LTTB (Largest-Triangle-Three-Buckets) and MinMaxLTTB algorithms.

Technically speaking, time-series downsampling generally falls into two categories¹:

• Characteristic Preserving: The goal is to keep statistical properties (like mean, variance, etc.) as consistent as possible before and after downsampling.
• Value Preserving: This approach selects representative data points directly from the raw dataset to preserve the overall visual shape.

Both LTTB and MinMaxLTTB are essentially value-preserving algorithms—their core objective is to retain the original trend and shape of the data after downsampling.