event-forecasting

Probabilistic forecasting estimates the likelihood of future events by applying Bayesian models and survival analysis to historical data. This approach generates probability distributions for event timing rather than single point estimates, ensuring accurate time-to-event predictions.

Bayesian time-to-event modeling predicts outcomes by validating multiple forecasting methods against historical data. It selects appropriate models based on data availability and event characteristics to generate accurate probabilistic forecasts for future occurrences.

Yes, this event forecasting framework requires Python libraries including PyMC, ArviZ, and Bambi. These dependencies provide the foundational Bayesian modeling, exploratory analysis, and high-level statistical interfaces needed to run probabilistic survival analysis.

Yes, you can use Bayesian forecasting to predict product launch success by a specific date. By analyzing historical data and market trends, the model generates a forecast with associated probabilities for the event occurring within your specified timeframe.

Probabilistic time-to-event forecasting handles limited data by offering a variety of methods based on data availability and event characteristics. It validates selected approaches against available historical data to ensure accurate probability distributions.

Choose Bayesian survival analysis when you need probability distributions for event timing rather than deterministic point estimates. It is suited for time-to-event questions across various domains where understanding the likelihood of occurrence is critical.