A professionally curated list of awesome resources (paper, code, data, etc.) on Transformers in Time Series, which is first work to comprehensively and ...
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Jan 8, 2024 · In this article, we will explore how we can modify a basic transformer model for time series classification task and, understand the basic ...
Aug 2, 2023 · The way transformers calculate multi-head self-attention is problematic for time series. Because data points in a series must be multiplied by ...
Jun 25, 2021 · Our model processes a tensor of shape (batch size, sequence length, features) , where sequence length is the number of time steps and features ...
Feb 15, 2022 · From the perspective of applications, we categorize time series Transformers based on common tasks including forecasting, anomaly detection, and ...
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This is the configuration class to store the configuration of a TimeSeriesTransformerModel. It is used to instantiate a Time Series Transformer model according ...
Hi, I'm a novice here in Kaggle. I'm trying to build a model for predictive maintenance. As is known, in this type of problem one often has to deal with ...
May 23, 2024 · In the class-specific module, we introduce the discovery method to extract the discriminative subsequences of each class (i.e. shapelets) from ...
We present a transformer-based network for time-series classification on various data modalities. Time series data for each modality is segmented at a fixed ...
We explore a class of problems involving classification and prediction from time-series data and show that recurrence combined with self-attention can meet or.
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