We have developed an advanced Long Short-Term Memory (LSTM) based time series forecasting model specifically designed to predict dengue cases in Sri Lanka by incorporating critical climate factors such as temperature and rainfall. This model helps in identifying potential dengue outbreaks well in advance, allowing for timely interventions and prevention measures.

Conducted thorough data preprocessing, including normalization and sequence creation, to prepare the dataset for optimal model performance.

Incorporated climate factors like temperature and rainfall, along with their respective lags, to enhance the prediction accuracy of dengue cases.

Built a robust LSTM-based model capable of capturing long-term dependencies and complex patterns in the data related to dengue cases.

Fine-tuned key hyperparameters such as LSTM units, dropout rates, batch size, and learning rate to optimize model performance.

Trained the model on historical dengue and climate data to ensure it learns effectively from the patterns.

Implemented a validation split to prevent overfitting and ensure the model generalizes well to unseen data.

Conducted comprehensive performance evaluations using RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error) metrics to demonstrate the model's accuracy and reliability.

Generated predictions for the test dataset and provided an 8-week ahead forecast to showcase the model’s forecasting capabilities.

Developed a detailed dashboard to visualize insights, predictions, and risk maps for effective decision-making.

Tailored the model to fit specific data and requirements, providing both short-term and long-term forecasts for dengue cases.

Provided detailed performance evaluations using industry-standard metrics to ensure high accuracy and reliability.

Offered guidance and support throughout the implementation process to ensure seamless integration and optimal results.

You provide the time series dataset along with any specific climate factors or features you'd like to include.

We develop and fine-tune an LSTM-based model customized to your data.

The model is trained and validated using your dataset to ensure accuracy and robustness.

Detailed performance metrics and insights are provided to demonstrate the model's effectiveness.

Precise and actionable forecasting results for your time series data are delivered, with a focus on predicting dengue cases.