# From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers

arXiv: https://arxiv.org/abs/1809.06751 · HTML: https://mlsanity.com/paper/1809.06751/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2018.

## Built on by (most cited first, 14 of 14)

- [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)
- [HYDRA: Competing convolutional kernels for fast and accurate time series classification](https://mlsanity.com/paper/2203.13652/) (2022)
- [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)
- [WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification](https://mlsanity.com/paper/2301.10194/) (2023)
- [An Efficient Federated Distillation Learning System for Multi-task Time Series Classification](https://mlsanity.com/paper/2201.00011/) (2021)
- [Fast, Accurate and Interpretable Time Series Classification Through Randomization](https://mlsanity.com/paper/2105.14876/) (2021)
- [Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series](https://mlsanity.com/paper/2304.05800/) (2023)
- [Plotting time: On the usage of CNNs for time series classification](https://mlsanity.com/paper/2102.04179/) (2021)
- [Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion](https://mlsanity.com/paper/2308.01071/) (2023)
- [FedST: Secure Federated Shapelet Transformation for Time Series Classification](https://mlsanity.com/paper/2302.10631/) (2023)
- [A Dictionary-based approach to Time Series Ordinal Classification](https://mlsanity.com/paper/2305.09288/) (2023)
- [Fuzzy Cognitive Maps and Hidden Markov Models: Comparative Analysis of Efficiency within the Confines of the Time Series Classification Task](https://mlsanity.com/paper/2204.13455/) (2022)

## Abstract (arXiv)

A family of algorithms for time series classification (TSC) involve running a sliding window across each series, discretising the window to form a word, forming a histogram of word counts over the dictionary, then constructing a classifier on the histograms. A recent evaluation of two of this type of algorithm, Bag of Patterns (BOP) and Bag of Symbolic Fourier Approximation Symbols (BOSS) found a significant difference in accuracy between these seemingly similar algorithms. We investigate this phenomenon by deconstructing the classifiers and measuring the relative importance of the four key components between BOP and BOSS. We find that whilst ensembling is a key component for both algorithms, the effect of the other components is mixed and more complex. We conclude that BOSS represents the state of the art for dictionary based TSC. Both BOP and BOSS can be classed as bag of words approaches. These are particularly popular in Computer Vision for tasks such as image classification. Converting approaches from vision requires careful engineering. We adapt three techniques used in Computer Vision for TSC: Scale Invariant Feature Transform; Spatial Pyramids; and Histrogram Intersection. We find that using Spatial Pyramids in conjunction with BOSS (SP) produces a significantly more accurate classifier. SP is significantly more accurate than standard benchmarks and the original BOSS algorithm. It is not significantly worse than the best shapelet based approach, and is only outperformed by HIVE-COTE, an ensemble that includes BOSS as a constituent module.