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Simultaneously, deep learning-based approaches for Multivariate Time Series Classification (MTSC) have garnered increasing attention due to their ability to automatically learn features from time series data, thus avoiding the cumbersome feature extraction process.
6 days ago
5 days ago · This paper explores novel ways of predicting and classifying the dynamics of piecewise smooth maps using various deep learning models.
2 days ago · Therefore, we designed a novel self-learning model to classify failures in multivariate time-series. We first used unsupervised learning to recognize faulty ...
3 days ago · Sequencing, long-term patterns, and temporal context within time-series datasets make them ideal training data for predictive, classification, and forecasting ...
Duration: 37:11
Posted: 6 days ago
Posted: 6 days ago
2 days ago · TBATS is a time series forecasting algorithm for univariate time series data. It is a hybrid algorithm that combines exponential smoothing and ARIMA models, ...
4 days ago · Multivariate time series classification (MTSC) is a fundamental data mining task, which is widely applied in the fields like health care and energy ...
3 days ago · The present study aimed to improve the transparency of DL-based gait classification, with acceleration time-series obtained during a 3-min walking task. The ...
2 days ago · Linear Regression with Time-Series Data. In this article, we will build a linear model with Time-Series data. We have to create a linear model to predict the ...
3 hours ago · One of the main practical problems in the classification of modern time series is the problem of temporal and spatial complexity of data. In general, dealing ...