Multivariate time series classification dataset. aeon formatted ts files (about 1.

Multivariate time series classification dataset , classification. [1] : Fast Channel Selection for Scalable Multivariate Time Series Classification Link Dec 1, 2024 · Transformer-based deep learning methods have significantly facilitated multivariate time series classification (MTSC) tasks. Data Source: Link Here: Description: This data was originally part of a competition The data is generated by sensors on the hands, elbows, wrists and thumbs. Jan 23, 2023 · Accuracy is a key focus of current work in time series classification. Inspired by the success of Natural Language Processing (NLP) benchmark datasets in advancing pre-trained models, we propose a new approach to create a comprehensive benchmark dataset for time series analysis. The maintainers of the archive also recommend reading the following papers "The UEA multivariate time series classification archive, 2018" and "Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress" before using the dataset. To address this issue, the causal and local correlations based network (CaLoNet) is proposed in this study for multivariate time Dec 2, 2024 · 3. The [1] proposed a fast channel selection technique for Multivariate Time Classification. timeseriesclassification. Oct 16, 2017 · Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT) Sep 27, 2018 · The dataset comes from a multivariate time series process. Time series takes the data vector and each data is connected with timestamp value as given by the user. The success of data-driven time series classification methods is largely attributed to the availability of large-scale data, which has facilitated the development of highly accurate models for real-world applications. 5 GB). Dec 20, 2023 · We work with 3 synthetic multivariate time series classification datasets and 2 real-world ones. The simplicity of this dataset allows us to demonstrate effectively how to use ConvNets for timeseries classification. Dataset listing. 2022. This repository contains a financial-domain-focused dataset for financial sentiment/emotion classification and stock market time series prediction. Yan, and T. Nonetheless, several algorithms for multivariate time series classification have been published. This dataset includes diverse variables capturing detailed operational data, repair records, and specifications of trucks while maintaining confidentiality by anonymization. Time series classification from scratch with deep neural networks: A strong baseline. It is well-suited for a range We consider each day in this database as a single time series of dimension 963 (the number of sensors which functioned consistently throughout the studied period) and length 6 x 24=144. Apr 24, 2022 · [23] TapNet: Multivariate Time Series Classification with Attentional Prototypical Network [24] Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets [25] Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals Dec 18, 2020 · Time Series Classification (TSC) involves building predictive models for a discrete target variable from ordered, real valued, attributes. 4. proposed transforming the existing univariate time series classification models, the Long Short Term Memory (LSTM) for Multivariate Time Series Classification . To solve this problem, we propose a Hierarchical Graph Embedding Dataset listing. We remove public holidays from the dataset, as well as two days with anomalies (March 8th 2009 and March 9th 2008) where all sensors were muted between 2:00 and Mar 17, 2024 · The new archive contains a wide range of problems, including variable length series, but it still only contains univariate time series classification problems. Sep 24, 2020 · We propose a novel approach to structural health monitoring (SHM), aiming at the automatic identification of damage-sensitive features from data acquired through pervasive sensor systems. For the synthetic datasets, we use the multivariate time series classification benchmark by Ismail et al. All python packages needed are listed in pip-requirements. Feb 13, 2023 · We validate this claim by evaluating multiple state-of-the-art deep learning-based TSC models on the University of East Anglia (UEA) benchmark, a standardized collection of 30 Multivariate Time Series Classification (MTSC) datasets. A real-world dataset is provided from a pulp-and-paper manufacturing industry. Here, we apply our framework for the tasks of multivariate time series regression and classification on sev- Mar 23, 2023 · Karim et al. IEEE, 760–769. py will read the data and return the data as the python list. In October 2018 more datasets were added, bringing the total to 128. The data were recored from ten subjects under three different conditions: normal (unbraced) walking on a treadmill, walking on a treadmill with a knee-brace on the right knee, and walking on a Download all of the new 30 multivariate UEA Time Series Classification datasets. A supervised training of the proposed network architecture is performed on Jan 29, 2021 · I am relatively new to time-series classification and am looking for some help: I have a dataset with 5000 multivariate time series each consisting of 21 variables, a time period of 3 years, and the class information of either 1 or 0. Based on some of your requests, we are planning to release additional posed time series classi cation (TSC) algorithms. py for a list of possible options). Time series classification is usually categorized into two types: univariate time series classification and multivariate time series (MTS) classification. A real world dataset is provided from a pulp-and-paper manufacturing industry. Despite the limited size of these datasets, we achieved classification accuracy improvements in 10 out of 13 datasets using the ROCKET and InceptionTime models. com, read this paper for details. Interpretable multivariate time series classifiers have been recently introduced, but none can maintain sufficient levels of efficiency and Oct 1, 2014 · Classification of multivariate time series data, often including both time points and intervals at variable frequencies, is a challenging task. An important application when we talk about time series classification is anomaly detection which is The Multivariate-Mobility-Paris dataset comprises information from 2020-08-24 to 2020-11-04 (72 days during the COVID-19 pandemic), with time granularity of 30 minutes and spatial granularity of 6 coarse regions in Paris, France. The format is same for the different datasets. Aug 1, 2024 · The UEA multivariate time series classification archive comprises real-world multivariate time series data. The univariate and multivariate classification problems are available in three formats: Weka ARFF, simple text files and aeon ts format. In R, it can be easily done by the ts() function with some parameters. Over recent years, a new set of TSC algorithms have been Jan 25, 2023 · Datasets The UEA MTS classification archive 2 consists of real-world multivariate time series data collected from different applications, such as human activity recognition, motion classification, and ECG/EEG (electroencephalogram) signal classification The dimension of the tested MTS ranges from two dimensions in trajectory classification data Nov 4, 2024 · In recent years, there has been widespread adoption of time series classification methods across various domains [], []. . Read the TSV data. This project concerns multivariate time-series classification for human activity recognition. This is particularly challenging in the domain of time series classification, where datasets may contain fewer than 100 training examples. This is a great benefit in time series forecasting, where classical linear methods can be difficult to adapt to multivariate or multiple input forecasting problems. Data collection period: Jan 2020 - Dec 2020 Number of Jun 10, 2024 · Our study investigates the impact of data augmentation on the performance of multivariate time series models, focusing on datasets from the UCR archive. Multivariate time series¶ Most of the literature for time series classification is focused on univariate time series. As the number of models increases, it's significant to design a unified framework to implement and evaluate these models. Although variable interaction relationships and sample similarity relationships exist in multivariate time series, the available related methods usually ignore the rich relationships and are ineffective in exploiting these. The overall goal is to identify a time series as coming from one of possibly many sources or predefined groups, using labeled training data. His archive becomes widely used in the community for benchmarking multivariate time series classification. A time series is a collection of data points ordered in time (Adhikari & Agrawal,2013). We implemented a Takens embedding for multivariate time series to convert the time series to point clouds and then extract TDA features (Betti numbers) at various values of a filtration parameter to represent the persistent homology of these data. Multivariate time series dataset for space weather data analytics. Getting data from Sep 5, 2023 · Multivariate time series classification has become popular due to its prevalence in many real-world applications. 1 Similarity-Based Methods Oct 9, 2023 · Yue et al. In 2017 International Joint Conference on Neural Networks (IJCNN May 26, 2020 · A time series can be classified into univariate and multivariate time series. Wang, W. Within each category of anomaly, N samples are selected as the support set, and M samples are chosen as the query set. 2 PAPERS • NO BENCHMARKS YET Apr 1, 2024 · To perform the classification of multivariate time series and to test how the application of MultiBEATS affects the performance in terms of accuracy and time, we have used a state-of-the-art method that consists of transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network We use 3D numpy even if the data is univariate: even though classifiers can work using a 2D array of shape (n_cases, n_timepoints), this 2D shape can get confused with single multivariate time series, which are of shape (n_channels, n_timepoints). Unfortunately, DL-based methods ignore explicit inter-series correlation modeling. Assumed a time series signal contains T time stamps and n sensors at each time stamp, the data file would include T lines, and each line has n real number splited by comma. Our In this githup repo, we provide four data sets could be used for researches related to the multivariate time series signals. Both the works of Ismail et al. The main focus has been on univariate TSC, i. Feb 11, 2022 · We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. 1 Synthetic Datasets. Oct 20, 2020 · Neural networks like Long Short-Term Memory (LSTM) recurrent neural networks are able to almost seamlessly model problems with multiple input variables. We chose HAR, PAM, and eight datasets of the Baydogan’s archive, a set of well-known benchmarks that contains 13 multivariate time series classification datasets. The primary purpose of the data is thought to be building a classification model Aug 1, 2021 · In the field of bag-of-words, Word ExtrAction for time SEries cLassification plus Multivariate Unsupervised Symbols and dErivatives (WEASEL + MUSE) [36] is considered the state-of-the-art, as it obtains the best results against its direct competitors: Learned Pattern Similarity (LPS) [8], AutoRegressive Forests for multivariate time series Oct 31, 2018 · Research into multivariate time series classification, where more than one series are associated with each class label, is in a position where univariate TSC research was a decade ago. A univariate time series data consists of only single observation recorded over time, while a multivariate time series consists of more than one observation, related to our subject of interest. According to a review on algorithms of multivariate time series classification , we classify them into three main types: similarity-based method, feature-based method and deep learning method. Recently, deep learning (DL) and graph neural network (GNN) methods have been applied to MVTSC tasks. One of the motivations for introducing the archive was to encourage researchers to perform a more rigorous evaluation of newly proposed time series classification (TSC) algorithms. Jan 27, 2022 · Neural networks are capable of learning powerful representations of data, but they are susceptible to overfitting due to the number of parameters. However, TS2Vec is designed to learn only feature-agnostic temporal representation. The raw data consist on eleven (11) multivariate time-series (MTS) measured for 150 seconds and collected during different levels of the fabrication process to describe the wafers Jan 5, 2020 · A real-world dataset is provided from the pulp-and-paper manufacturing industry. Here we implement Fast Fourier, Spectral density, Autocorrelation signal processing techniques on Oct 24, 2024 · The classification of MTS is a common challenge with wide-ranging applications across different fields. In other words, it represents a multivariate time series dataset. The data contains a rare event of paper break that commonly occurs in the industry. It has worked: most recent research into TSC uses all 85 datasets to evaluate algorithmic advances [2]. It is derived from sound. The performance of a DL-based MTSC algorithm is heavily dependent on the quality of the learned representations providing semantic information for downstream tasks, e. In Fig. ) and diseases (respiratory diseases, diarrhea, etc. XEM relies on a new hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by machine learning models and an implicit divide-and-conquer approach to individualize classifier errors on different parts of the training data. (2022) Ge Gao, Qitong Gao, Xi Yang, Miroslav Pajic, and Min Chi. In real life, multiple sensors often work together. Aug 1, 2024 · We selected ten datasets to test the performance of the MTS2Graph framework and compare it to the baselines and state-of-the-art methods. Despite the limited size of these datasets, we achieved classification accuracy improvements in 10 out of 13 datasets using the Rocket and InceptionTime models. The archive consists of 15 datasets in "mat" format, therefore, multivarReader. The MVTS instance \(mvts_i \in \mathbb {R}^{T \times N}\) is a collection of individual time series of N magnetic field parameters, where each time series contains periodic observation values of the corresponding parameter for an observation period T. If you are interested in multivariate (multi-dimensional) time series, there is a collection of thirty such datasets archived here: www. A great source of multivariate time series data is the UCI Machine Learning Repository. We introduce the KarmaLegoSification (KLS) framework for classification of multivariate time series analysis, which implements three phases: (1) application of a temporal abstraction process that transforms a series of raw time-stamped data points into . As we primarily address MTS generated from automatic sensors with a •xed and synchronized sampling along all dimensions, we can safely ignore time stamps. The focus of this article will be on multivariate data. This highlights the essential Nov 27, 2024 · Recently, time series classification has attracted the attention of a large number of researchers, and hundreds of methods have been proposed. Sep 27, 2018 · A real-world dataset is provided from a pulp-and-paper manufacturing industry that contains a rare event of paper break that commonly occurs in the industry and can be used for multivariate time series data exploration and building other supervised and unsupervised models. Dec 14, 2022 · This dataset is a six dimensional array of joint angle data: 10 subjects x 3 conditions x 10 replications x 2 legs x 3 joints x 101 time points. TS emerge in many scientific and commercial applications, like weather observations, wind energy forecasting, industry automation, mobility tracking, etc. The package includes functions allowing the execution of clustering, classification or outlier detection methods, among others. 5w Seizure Movement: introduced LAXCAT, a novel, modular architecture for explainable multivariate time series classification. and that of Ruiz et al. The data contains sensor readings at regular time-intervals (x's) and the event label (y). I have a table that takes snapshots of ObjectID over time, and I want to use current data to predict if the ObjectID will convert (1 or 0) in the coming 3 The PhysioNet Challenge 2012 dataset is publicly available and contains the de-identified records of 8000 patients in Intensive Care Units (ICU). We will use the FordA_TRAIN file for training and the FordA_TEST file for testing. Channel Selection in Multivariate Time Series Classification# Overview# Sometimes every channel is not required to perform classification; only a few are useful. Most existing GNN-based methods treat MVTS data as a static graph spanning the entire temporal Jul 10, 2020 · The NWRA Classification Infrastructure: description and extension to the Discriminant Analysis Flare Forecasting System (DAFFS). The length of the datasets ranges from 8 to 17 , 984 , and the dimension values range from 2 to 1345. This dataset was originally used in paper "A general framework for never-ending learning from time series streams", DAMI 29(6). However, these methods often ignore the spatial correlations among dimensions and the local correlations among features. Assuming you have docker installed. Current multivariate time series classification (MTSC) algorithms need hundreds of compute hours to complete training and prediction. The solar event instance i is represented by an MVTS instance \(mvts_i\). After that, 5,000 heartbeats were randomly selected. Keywords Deep learning Time series Classi cation Review 1Introduction During the last two decades, Time Series Classi cation (TSC) has been considered as one of the Circulation 101(23)". recently proposed bespoke Multivariate Time Series Classification (MTSC) algorithms based on deep learning [40, 41]. Download all of the new 30 multivariate UEA Time Series Classification datasets. The analysis of this data is very beneficial in many domains, such as weather forecasting (Shumway et al. It's based on our paper: StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series accepted by AAAI 2023 Bridge (AI for Financial Services). However, speed and data reduction are equally important in many applications, especially when the data scale and storage requirements rapidly increase. Note, that we consider only MTS with numerical a−ributes (not categorical). In this problem, we have a similar multivariate time series data from a pulp-and-paper industry with a rare event associated Feb 17, 2024 · Time Series in R programming language is used to see how an object behaves over a period of time. They are commonly found in manufacturing processes that have several sensors collecting the data in over time. Some of these datasets are stored in GitHub data Nov 27, 2023 · This paper presented a TDA-based multivariate time series classification method for long time series. The data are the x,y,z coordinates for each of the eight locations. 1 Notations. the problem where each case has a single series and a class label Download all of the new 30 multivariate UEA Time Series Classification datasets. Jan 1, 2022 · It proposes an efficient multivariate time-series fault detection approach aiming to detect wafer anomalies at an early fabrication stage to reduce the yield loss. You can now use the docker image provided here. e. Multivariate Time Series Datasets. txt files) (about 2 GB). Each TS is constructed from one of the UEA & UCR time series classification datasets. This is due to the nature of Oct 14, 2024 · Time series analysis has become increasingly important in various domains, and developing effective models relies heavily on high-quality benchmark datasets. ) is a promising way to multivariate time series through an input “denoising” (autoregres-sive) objective. The new archive contains a wide range of problems, including variable length series, but it still only contains univariate time series Modeling multivariate time series has been a subject for a long time, which attracts the attention of scholars from many fields including economics, finance, traffic, etc. The Time Series Classification (TSC) task involves training a model from a collection of time series (real valued, ordered, data) in order to predict a target variable. We select 26 datasets with equal lengths in the time series of each dimension. [18] proposed a universal representation learning framework, TS2Vec, which achieved state-of-the-art performance on 29 multivariate time-series classification datasets and four multivariate time-series forecasting benchmarks. What I want to do is to classify a new input consisting itself of 21 variables over a time period of 3 years. It gradually expanded, until 2015 when it increased in size from 45 datasets to 85 datasets. archive) and 12 multivariate time series datasets. A multivariate time series (MTS) is produced when multiple correlated streams of data are recorded over time. May 24, 2024 · Disjoint-cnn for multivariate time series classification. However, most state-of-the-art focuses on improving classification performance, with the best-performing models typically opaque. The data contains sensor readings at regular time-intervals (x’s) and the event label (y). g. Gao et al. txt file ofm interconnected sensors recording values at each time instant. The primary purpose of the data is thought to be building a classification model Aug 1, 2023 · Time series classification aims to assign labels to time series through supervised learning [1]. Disc Oct 31, 2018 · In 2002, the UCR time series classification archive was first released with sixteen datasets. In 2021 International Conference on Data Mining Workshops (ICDMW). New datasets: we have increased the number of datasets you can download using tsai: 128 univariate classification datasets; 30 multivariate classification datasets; 15 regression datasets; 62 forecasting datasets; 9 long term forecasting datasets; New tutorials: PatchTST. Sep 27, 2018 · A real-world dataset is provided from a pulp-and-paper manufacturing industry. The archive can be referenced with this paper. We also provide simple utilities to extend algorithms for univariate time series to multivariate time series. Dealing with this high-dimensional data is challenging for every classifier for at least two aspects: First, an MTS is not only characterized by individual feature values, but also by the interplay of features in different dimensions. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series classification model by augmenting the fully convolutional block with a squeeze-and Jun 10, 2024 · Our study investigates the impact of data augmentation on the performance of multivariate time series models, focusing on datasets from the UCR archive. Algorithms are evaluated using very few datasets and claims of improvement are not based on statistical comparisons. The full description of this dataset can be found here. Multivariate datasets are generally more challenging and are the sweet spot for machine learning methods. This is because the sequence length of each instance in the same dataset can be in arbitrary lengths. Weka does not allow for unequal length series, so the unequal length problems are all padded with missing values. Mar 1, 2021 · Time Series Classification (TSC) involves building predictive models for a discrete target variable from ordered, real valued, attributes. The problem is a balanced binary classification task. Know. We group TS by label and concatenate them to create segments with distinctive temporal patterns and statistical properties. In this paper, we show that the simple methods of cutout, cutmix, mixup, and window warp improve the Jan 26, 2024 · This paper presents a description of a real-world, multivariate time series dataset collected from an anonymized engine component (called Component X) of a fleet of trucks from SCANIA, Sweden. (Y Chen, E Keogh, B Hu, N Begum, A Bagnall, A Mueen and G Batista, 2015) One driving force behind their rising importance is the sharply increasing use of Sep 8, 2020 · An univariate time series is an ordered set of real values, while a M dimensional multivariate time series consists of M different univariate time series with the same length. aeon formatted ts files (about 1. In this tutorial, you will discover how you can […] Mar 26, 2021 · et al. imbalance is critical, especially in multivariate time series datasets where the complexity and variability of data ex-acerbate the problem. It also incorporates a collection of multivariate time series datasets which can be used to analyse the performance of new proposed algorithms. Therefore, the MTS is widely used daily. ,2000). We show that adding the proposed attention blocks improves base models' average accuracy by up to 3. Over recent years, a new set of TSC algorithms have been developed which have made significant improvement over the previous state of the art. 2 we present one sample from one synthetic dataset and one sample each for the real-world datasets. The dataset comes from a multivariate time Mar 8, 2024 · A time series (TS) is a collection of values sequentially ordered in time. At the time of writing, there are 63 time series datasets that you can download for free and 25/05/2023:Bake off redux: a review and experimental evaluation of recent time series classification algorithms arXiv, 2023 02/05/2023:Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression arXiv, 2023 21/03/2023:Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classification Data Min. Time Series Classification is a general task that can be useful across many subject-matter domains and applications. Apr 25, 2022 · Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Time Intervals: WSDM: 2021-PM2. Weka formatted ARFF files (and . This highlights the essential role of sufficient data in training effective models Apr 1, 2024 · The dataset used for classification is specifically partitioned and denoted as W P, and this dataset solely comprises labeled anomalous multivariate time series. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date. 6%. Sep 11, 2020 · Our approach, KMTS, is based on (i) the computation of multiple yet simple representations of time series, and their storage in a relational data scheme, (ii) a recently suggested approach for relational data classification [] using feature construction through propositionalisation and, supervised feature selection and classification through a selective Naïve Bayes classifier []. Jan 9, 2024 · Multivariate time series classification (MTSC) based on deep learning (DL) has attracted increasingly more research attention. Oct 31, 2018 · Research into multivariate time series classification, where more than one series are associated with each class label, is in a position where univariate TSC research was a decade ago. Although deep learning methods have demonstrated potential in this area, graph neural networks (GNNs) have become a effective method for capturing the complex interconnections within MTS data. Download the dataset. 2. , 2017] Z. Re-search into multivariate time series classi cation, where more than one series are associated with each class label, is in a position where univariate TSC research was a decade ago. Multivariate time series classification (MVTSC) has significant potential for Internet of Things applications. Our study concentrates on the UCR archive, which has recently been enriched with a broad array of multivariate time series datasets, offering an ideal environment 10. I have a multivariate time series dataset where I would like to predict the likelihood of an outcome of 1/0 (think of this as a Conversion) in the next 3 time periods for unique ObjectID’s. Oates. Hence, a model’s representation learning ability is critical for enhancing its Multivariate time series classification aims to determine the labels for multivariate time series samples. ts format does allow for this feature. A reinforcement learning-informed pattern mining framework for multivariate time series classification. This paper explores the Oct 24, 2024 · In multivariate time series classification, although current sequence analysis models have excellent classification capabilities, they show significant shortcomings when dealing with long sequence multivariate data, such as prolonged training times and decreased accuracy. The dataset comes from a multivariate time series process. For example, we might want to build a model that can predict whether a patient is sick based on the ECG reading, or predict whether a device will fail based on some sensor reading. Nov 30, 2017 · Multivariate time series (MTS) arise when multiple interconnected sensors record data over time. The data was pre-processed in two steps: (1) extract each heartbeat, (2) make each heartbeat equal length using interpolation. Perceptron (Neuron) The time series segmentation benchmark (TSSB) currently contains 75 annotated time series (TS) with 1-9 segments. which means we are launching the fcn model on the univariate UCR archive for the Coffee dataset (see constants. Damage detection and localization are formulated as classification problems, and tackled through fully convolutional networks (FCNs). A time series dataset D contains N time series. A Time Series Classification problem is a Classification problem where the objects of the dataset are univariate or multivariate time series. The pre-trained model can be subsequently applied to several downstream tasks, such as regression, classification, im-putation, and forecasting. Dataset from 8800 (10 digits x 10 repetitions x 88 speakers) time series of 13 Frequency Cepstral Coefficients (MFCCs) had taken from 44 males and 44 females Arabic native speakers between the ages 18 and 40 to represent ten spoken Arabic digit. However, due to the inherent operation of self-attention mechanism, most existing methods tend to overlook the internal local features and temporal invariance of time series, potentially resulting in a limited understanding of the representation and context information Aug 20, 2024 · Abstract page for arXiv paper 2409. 00053: Accelerometer-Based Multivariate Time-Series Dataset for Calf Behavior Classification Getting new insights on pre-weaned calf behavioral adaptation to routine challenges (transport, group relocation, etc. Jan 14, 2018 · Over the past decade, multivariate time series classification has received great attention. Each record consists of roughly 48 hours of multivariate time series data with up to 37 features recorded at various times from the patients during their stay such as respiratory rate, glucose etc. more info. Aug 1, 2024 · In this section, we briefly introduce the work progress in multivariate time series classification. Target activities are compromised of 'Walking', 'Upstairs', 'Downstairs', 'Sitting', 'Standing', 'Lying'. multivariate time series. Method 1: Using Basic R met This data set is taken from the UCI repository. vtmuj yngpi miya ufqur fjtrwr muufr tyzul hxpoha nnec mkbp