retroprediction

Retroprediction is a term used in statistics, data science, and related fields to describe the inference of past states or events from present-day observations, typically by applying and reversing a forward model. The concept is closely related to retrodiction, but some authors distinguish retrodiction as the direct inference of historical states from current data, while retroprediction emphasizes the use of predictive models to reconstruct historical conditions or to explain observed data retroactively.

In practice, retroprediction involves formulating an inverse problem: given observations, estimate past parameters, inputs, or states that could have produced them. Common tools include Bayesian inference, maximum likelihood, and other probabilistic methods, often implemented within state-space models or inverse problems. Projects range across disciplines, such as archaeology reconstructing past cultures from artifacts, climate science inferring historical temperatures from proxies, cosmology estimating early-universe conditions from current measurements, and forensic science retracing events from evidence.

Challenges include non-uniqueness of solutions, ill-posedness, sensitivity to measurement errors and prior assumptions, and model misspecification. Robust retroprediction requires careful uncertainty quantification, validation against independent evidence, and transparent reporting of priors and assumptions. While retroprediction shares mathematical foundations with forward forecasting, it raises distinct epistemic issues about identifiability and the reliability of reconstructed histories.