Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.

scores: Metrics for the verification, evaluation and optimisation of forecasts, predictions or models.

Utilities for Scoring and Assessing Predictions

Comparing sequential forecasters via confidence sequences & e-processes

Easily evaluate your forecasts with (multivariate) Diebold-Mariano and (multivariate) Giacomini-White tests of equal predictive ability and MCS.

Multi Horizon Superior Predictive Ability (SPA) test proposed by Quaedvlieg (2021)

This code mainly computes the forecast of headline inflation using different aproaches. Likewise presents the forecast evaluation for each model along different points in a span period.

Reference implementation of the Relative Utility Value metric for forecast value assessment

Supplementary materials for the following publication: Davydenko, A., & Goodwin, P. (2021). Assessing point forecast bias across multiple time series: Measures and visual tools. International Journal of Statistics and Probability, 10(5), 46-69. https://doi.org/10.5539/ijsp.v10n5p46

self archived publications

Analysis on the quality and determinants of economic forecasts pre-Covid 19

Analysis on the quality and determinants of economic forecasts during the Covid 19 pandemic

Forecast Evaluation Package for gretl

Successfully established an LSTM model to effectively forecast global equity based on over 20+ years of historical data of global equity.