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 underlying logics on ...

May 23, 2024 · In this paper, we propose a novel Shapelet Transformer (ShapeFormer), which comprises class-specific and generic transformer modules to capture both of these ...

Dec 1, 2023 · Transformers are good when there are long-range dependencies, in those cases they outperform the LSTMs. Main key feature is High dimensional data in those cases ...

Oct 9, 2023 · I am trying to classify these series. For that I am first passing these tensors into a Transformer Encoder (having 2 attention heads and 2 encoder layers) that ...

Duration: 6:33
Posted: Nov 1, 2023

Nov 10, 2023 · We present a transformer-based dynamic architecture to achieve adaptive learning strategies for different frequency components of the time series data.

Sep 28, 2023 · The authors of 'Are Transformers Effective for Time Series Forecasting' demonstrated that Transformer models could be beaten by a very simple linear model. When ...

Dec 11, 2023 · Our model processes a tensor of shape (batch size, sequence length, features), where sequence length is the number of time steps and features is each input ...

Feb 4, 2024 · In this paper, we construct time series corpora from diverse domains, standardize them into a unified sequence format, and utilize a generative objective to pre ...