zamaex96/Hybrid-CNN-LSTM-with-Spatial-Attention
This documents the training and evaluation of a Hybrid CNN-LSTM Attention model for time series classification in a dataset. The model combines convolutional neural networks (CNNs) for feature extraction, long short-term memory (LSTM) networks for sequential modeling, and attention mechanisms to focus on important parts of the sequence.
This project helps domain experts classify time series data into different categories. It takes normalized time series data, typically from a CSV file, and outputs a classification label (one of four possible categories) for each series. It's designed for data analysts, researchers, or anyone working with sequential data who needs to automatically categorize or identify patterns within their time series measurements.
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Use this if you have normalized time series data and need to accurately classify it into predefined categories, leveraging advanced machine learning techniques.
Not ideal if your data is not time series, is unnormalized, or if you need to predict continuous values rather than discrete classes.
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Jan 14, 2025
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