# A Time Series Forest for Classification and Feature Extraction

arXiv: https://arxiv.org/abs/1302.2277 · HTML: https://mlsanity.com/paper/1302.2277/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2013.

## Built on by (most cited first, 30 of 69)

- [HIVE-COTE 2.0: a new meta ensemble for time series classification](https://mlsanity.com/paper/2104.07551/) (2021)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification](https://mlsanity.com/paper/2102.00457/) (2021)
- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Version](https://mlsanity.com/paper/2302.12721/) (2023)
- [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024)
- [An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set](https://mlsanity.com/paper/2305.11921/) (2023)
- [The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification](https://mlsanity.com/paper/2105.03841/) (2021)
- [BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning](https://mlsanity.com/paper/2306.13101/) (2023)
- [ClaSP -- Parameter-free Time Series Segmentation](https://mlsanity.com/paper/2207.13987/) (2022)
- [The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier](https://mlsanity.com/paper/2201.12048/) (2022)
- [QUANT: A Minimalist Interval Method for Time Series Classification](https://mlsanity.com/paper/2308.00928/) (2023)
- [An Efficient Federated Distillation Learning System for Multi-task Time Series Classification](https://mlsanity.com/paper/2201.00011/) (2021)
- [All-Clear Flare Prediction Using Interval-based Time Series Classifiers](https://mlsanity.com/paper/2105.01202/) (2021)
- [Cross-Domain Pre-training with Language Models for Transferable Time Series Representations](https://mlsanity.com/paper/2403.12372/) (2024)
- [Advancing Time Series Classification with Multimodal Language Modeling](https://mlsanity.com/paper/2403.12371/) (2024)
- [Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression](https://mlsanity.com/paper/2305.01429/) (2023)
- [Elastic Similarity and Distance Measures for Multivariate Time Series](https://mlsanity.com/paper/2102.10231/) (2021)
- [Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields](https://mlsanity.com/paper/2311.18029/) (2023)
- [End-to-End Signal Classification in Signed Cumulative Distribution Transform Space](https://mlsanity.com/paper/2205.00348/) (2022)
- [Appliance Detection Using Very Low-Frequency Smart Meter Time Series](https://mlsanity.com/paper/2305.10352/) (2023)
- [Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms](https://mlsanity.com/paper/2310.02812/) (2023)
- [Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification](https://mlsanity.com/paper/2410.18686/) (2024)
- [Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting](https://mlsanity.com/paper/2401.10359/) (2024)
- [Fast, Accurate and Interpretable Time Series Classification Through Randomization](https://mlsanity.com/paper/2105.14876/) (2021)
- [POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspective](https://mlsanity.com/paper/2309.08499/) (2023)
- [Time-Series Classification for Dynamic Strategies in Multi-Step Forecasting](https://mlsanity.com/paper/2402.08373/) (2024)
- [Explaining Time Series Classification Predictions via Causal Attributions](https://mlsanity.com/paper/2405.15871/) (2024)
- [Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion](https://mlsanity.com/paper/2308.01071/) (2023)
- [Active Region-based Flare Forecasting with Sliding Window Multivariate Time Series Forest Classifiers](https://mlsanity.com/paper/2402.03474/) (2024)

## Abstract (arXiv)

We propose a tree ensemble method, referred to as time series forest (TSF), for time series classification. TSF employs a combination of the entropy gain and a distance measure, referred to as the Entrance (entropy and distance) gain, for evaluating the splits. Experimental studies show that the Entrance gain criterion improves the accuracy of TSF. TSF randomly samples features at each tree node and has a computational complexity linear in the length of a time series and can be built using parallel computing techniques such as multi-core computing used here. The temporal importance curve is also proposed to capture the important temporal characteristics useful for classification. Experimental studies show that TSF using simple features such as mean, deviation and slope outperforms strong competitors such as one-nearest-neighbor classifiers with dynamic time warping, is computationally efficient, and can provide insights into the temporal characteristics.