Implementation of Multivariate Time Series Forecasting for Air Pollution using LSTMs in Keras

Multivariate Time Series Forecasting with LSTMs in Keras

multivariate time series forecasting using LSTMs(Air Pollution)

Built a multivariate LSTM model in Python to forecast air pollution using time series data. Preprocessed data with lag features, normalized inputs, and reshaped for supervised learning. Trained on multiple timesteps, evaluated with RMSE & MAE, and delivered a scalable forecasting pipeline.

LSTM-based deep learning model to forecast PM2.5 air pollution using multivariate time series data. The project includes data preprocessing, feature encoding, scaling, sequence generation, model training, and evaluation on real-world environmental datasets