MIHT: A Hoeffding Tree for Time Series Classification using Multiple Instance Learning

TL;DR

The paper introduces MIHT, an efficient and interpretable multi-instance Hoeffding Tree algorithm for classifying multivariate, variable-length time series, outperforming existing models on multiple datasets.

Contribution

It proposes a novel multi-instance learning approach with a new time series representation, enabling accurate and interpretable classification of complex, variable-length series.

Findings

Outperforms 11 state-of-the-art models on 28 datasets

Handles high-dimensional, variable-length time series effectively

Provides interpretable decision trees highlighting relevant variables and segments

Abstract

Due to the prevalence of temporal data and its inherent dependencies in many real-world problems, time series classification is of paramount importance in various domains. However, existing models often struggle with series of variable length or high dimensionality. This paper introduces the MIHT (Multi-instance Hoeffding Tree) algorithm, an efficient model that uses multi-instance learning to classify multivariate and variable-length time series while providing interpretable results. The algorithm uses a novel representation of time series as "bags of subseries," together with an optimization process based on incremental decision trees that distinguish relevant parts of the series from noise. This methodology extracts the underlying concept of series with multiple variables and variable lengths. The generated decision tree is a compact, white-box representation of the series' concept, providing interpretability insights into the most relevant variables and segments of the series. Experimental results demonstrate MIHT's superiority, as it outperforms 11 state-of-the-art time series classification models on 28 public datasets, including high-dimensional ones. MIHT offers enhanced accuracy and interpretability, making it a promising solution for handling complex, dynamic time series data.