Time Series Analysis with Facebook Prophet Python and Cesium

This project demonstrates time series forecasting using Prophet and the additional Cesium features. We try to use historic data trends and any seasonal nature of healthcare call data to prepare for predictions enhanced with external time series features. We would like to make accurate future predictions for this data by combining optimized feature extraction with forecasting capabilities inherent in Prophet.

Project Outcomes

✓This project leverages the Prophet model with Cesium
✓extracted features to enhance healthcare call volume forecasting accuracy. The reusable forecasting pipeline can be applied to various industries for better demand prediction and decision
✓making.
✓Using the Prophet model
✓accurately forecasted healthcare call volumes for the next 12 months.
✓Improved model accuracy by adding additional statistical features such as mean
✓standard deviation
✓and absolute difference.
✓To enhance the predictions
✓we add Cesium
✓extracted features as additional regressors to the Prophet model.
✓Better forecast trends
✓seasonality
✓and uncertainty intervals are visualized for better decision
✓making.
✓They were able to identify key patterns and trends in healthcare call data
✓such as seasonal peaks and troughs.
✓Forwards fill without breaking dataset continuity
✓to help handle missing data.
✓They generate reliable predictions and can be used to guide resource allocation and staffing needs of healthcare facilities.
✓Helped us understand what drove call volumes
✓seasonality
✓outliers
✓and all.
✓Built a reusable forecasting pipeline that can be used in other sectors or on other datasets.
✓It also demonstrated how combining Prophet and Cesium provides better time series forecasting accuracy.

Requirements:

  • →Knowledge of time series analysis and forecasting to a basic extent.
  • →Having some hands-on experience with Python and also manipulating data with pandas.
  • →Knowledge about the Prophet model for time series forecasting.
  • →Experience with Cesium for feature extraction from time series.
  • →Knowledge of basic statistical features such as mean, standard deviation, and skewness.
  • →Familiarity with data visualization using matplotlib and seaborn.
  • →Python packages: pandas, prophet, cesium, matplotlib, seaborn, numpy, scipy.

Project Description

The project aims to generate a predictive healthcare call data model using the Prophet, enhanced by features extracted by Cesium. The first step is cleaning the data, then followed by the extraction of important time series features—mean, standard deviation, and many others—using Cesium from the historical data before it's fed directly into the Prophet for forecasting. Future call volumes are predicted after training, which are visualized for interpretation. The trends, seasonality, and uncertainty intervals captured in the plots provide a comprehensive view of the forecast.

Time Series Analysis with Facebook Prophet Python and Cesium

Forecast healthcare call volumes using Prophet with enhanced features from Cesium. Improve accuracy with statistical features and seasonal patterns.

$20$15.0025% off