Industrial IoT Sensor Data Prediction Using LSTM

This code generates synthetic sensor data, trains an LSTM network on this data, and then predicts future readings for industrial IoT.
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Updated 5 Jun 2023

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This code employs a long short-term memory (LSTM) network to predict time-series sensor data. It generates synthetic data for three sensors: temperature, humidity, and vibration. Each sensor's data is represented as a sinusoidal function with added noise, closely simulating the variability and randomness found in real-world sensor data. Once trained, the LSTM network can predict future sensor values, demonstrating the practical utility of LSTM networks in monitoring and predictive tasks within IoT systems.

Cite As

Ardavan Rahimian (2024). Industrial IoT Sensor Data Prediction Using LSTM (https://www.mathworks.com/matlabcentral/fileexchange/130604-industrial-iot-sensor-data-prediction-using-lstm), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2023a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes
1.0