publications

2023

1. Scalable classifier-agnostic channel selection for multivariate time series classification

   Bhaskar Dhariyal, Thach Le Nguyen, and Georgiana Ifrim

   Data Mining and Knowledge Discovery, Mar 2023

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   Accuracy is a key focus of current work in time series classification. However, speed and data reduction are equally important in many applications, especially when the data scale and storage requirements rapidly increase. Current multivariate time series classification (MTSC) algorithms need hundreds of compute hours to complete training and prediction. This is due to the nature of multivariate time series data which grows with the number of time series, their length and the number of channels. In many applications, not all the channels are useful for the classification task, hence we require methods that can efficiently select useful channels and thus save computational resources. We propose and evaluate two methods for channel selection. Our techniques work by representing each class by a prototype time series and performing channel selection based on the prototype distance between classes. The main hypothesis is that useful channels enable better separation between classes; hence, channels with a larger distance between class prototypes are more useful. On the UEA MTSC benchmark, we show that these techniques achieve significant data reduction and classifier speedup for similar levels of classification accuracy. Channel selection is applied as a pre-processing step before training state-of-the-art MTSC algorithms and saves about 70% of computation time and data storage with preserved accuracy. Furthermore, our methods enable efficient classifiers, such as ROCKET, to achieve better accuracy than using no selection or greedy forward channel selection. To further study the impact of our techniques, we present experiments on classifying synthetic multivariate time series datasets with more than 100 channels, as well as a real-world case study on a dataset with 50 channels. In both cases, our channel selection methods result in significant data reduction with preserved or improved accuracy.

2. Classification of cow diet based on milk Mid Infrared Spectra: A data analysis competition at the “International Workshop on Spectroscopy and Chemometrics 2022”

   Maria Frizzarin, Giulio Visentin, Alessandro Ferragina, and 14 more authors

   Chemometrics and Intelligent Laboratory Systems, Mar 2023

   Abs

   In April 2022, the Vistamilk SFI Research Centre organized the second edition of the “International Workshop on Spectroscopy and Chemometrics – Applications in Food and Agriculture”. Within this event, a data challenge was organized among participants of the workshop. Such data competition aimed at developing a prediction model to discriminate dairy cows’ diet based on milk spectral information collected in the mid-infrared region. In fact, the development of an accurate and reliable discriminant model for dairy cows’ diet can provide important authentication tools for dairy processors to guarantee product origin for dairy food manufacturers from grass-fed animals. Different statistical and machine learning modelling approaches have been employed during the workshop, with different pre-processing steps involved and different degree of complexity. The present paper aims to describe the statistical methods adopted by participants to develop such classification model.

3. Fast Time Series Classification with Random Symbolic Subsequences

   Thach Le Nguyen, and Georgiana Ifrim

   In Advanced Analytics and Learning on Temporal Data, Mar 2023

   Abs

   Symbolic representations of time series have proven to be effective for time series classification, with many recent approaches including BOSS, WEASEL, and MrSEQL. These classifiers use various elaborate methods to select discriminative features from symbolic representations of time series. As a result, although they have competitive results regarding accuracy, their classification models are relatively expensive to train. Most if not all of these approaches have missed an important research question: are these elaborate feature selection methods actually necessary? ROCKET, a state-of-the-art time series classifier, outperforms all of them without utilizing any feature selection techniques. In this paper, we answer this question by contrasting these classifiers with a very simple method, named MrSQM. This method samples random subsequences from symbolic representations of time series. Our experiments on 112 datasets of the UEA/UCR benchmark demonstrate that MrSQM can quickly extract useful features and learn accurate classifiers with the logistic regression algorithm. MrSQM completes training and prediction on 112 datasets in 1.5 h for an accuracy comparable to existing efficient state-of-the-art methods, e.g., MrSEQL (10 h) and ROCKET (2.5 h). Furthermore, MrSQM enables the user to trade-off accuracy and speed by controlling the type and number of symbolic representations, thus further reducing the total runtime to 20 min for a similar level of accuracy. With these results, we show that random subsequences extracted from symbolic transformations can be as effective as the more sophisticated and expensive feature selection methods proposed in previous works. We propose MrSQM as a strong baseline for future research in time series classification, especially for approaches based on symbolic representations of time series.

2019

1. Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations

   Thach Le Nguyen, Severin Gsponer, Iulia Ilie, and 2 more authors

   Data Mining and Knowledge Discovery, Jul 2019

2017

1. Time Series Classification by Sequence Learning in All-Subsequence Space

   Thach Le Nguyen, Severin Gsponer, and Georgiana Ifrim

   In 2017 IEEE 33rd International Conference on Data Engineering (ICDE), Apr 2017