This article demonstrates how to use the attention mechanism in a time series classification framework, specifically for classifying normal sine waves versus 'modified' (flattened) sine waves. It details the data generation, model implementation (using a bidirectional LSTM with attention), and results, achieving high accuracy.
A new LSTM model, sLSTM, is introduced to improve long-term time series forecasting accuracy. It's evaluated on benchmark datasets and compared to other state-of-the-art methods.