Model-agnostic visual explanations via approximate bilinear models

Abstract

Visualization plays an important role in the analysis and exploration of time series data. To facilitate efficient visualization of large datasets, downsampling has emerged as a well-established approach. This work concentrates on LTTB (Largest-Triangle-Three-Buckets), a widely adopted downsampling algorithm for time series data point selection. Specifically, we introduce MinMaxLTTB, a two-step algorithm that significantly improves the scalability of LTTB. MinMaxLTTB consists of the following two steps: (i) the MinMax algorithm preselects a certain ratio of minimum and maximum data points, followed by (ii) applying the LTTB algorithm on only these preselected data points, effectively reducing LTTB's time complexity. The MinMax algorithm is computationally efficient and can be parallelized, enabling efficient data point preselection. Additionally, MinMax demonstrates competitive performance in terms of visual representation, making it also an effective data reduction method. Experimental results demonstrate that MinMaxLTTB outperforms LTTB by more than an order of magnitude in terms of computation time. Furthermore, preselecting a small multiple of the desired output size already yields similar visual representativeness compared to LTTB. In summary, MinMaxLTTB leverages the computational efficiency of MinMax to scale LTTB, without compromising on LTTB its favorable visualization properties. The code and experiments associated with this paper can be found at https://github.com/predict-idlab/MinMaxLTTB.