Conformal Prediction is a framework designed to quantify uncertainty, and it’s quickly becoming a favoured approach in both the corporate world and academic research. Its ease of application sets it apart; with minimal adjustments, it can be integrated into any predictive model. This allows for the generation of prediction intervals…

“In my article ‘Conformal Prediction Forecasting with MAPIE,’ we explored how Conformal Prediction enables us to construct powerful probabilistic forecasting models. Unlike other probabilistic forecasting methods, Conformal Prediction forecasting models can generate prediction intervals encapsulating ground truth values at user-determined coverage levels. …

A while back in my article ‘How to predict quantiles in a more intelligent way (or ‘Bye-bye quantile regression, hello Conformalized Quantile Regression’) I have covered Conformalized Quantile Regression — one of the most popular Conformal Prediction models. Today, we will look at another powerful Conformal Prediction model for quantifying…

In my previous article, “Deep Learning Is What You Do Not Need”, I explained that deep learning for time series is not the solution that most companies need even to consider; in this article, we delve deeper into more good reasons why transformers are not the solution that is effective…

Unlock powerful SSA methods to generate highly accurate forecasts. As a machine learning researcher and data science practitioner, I am always interested to learn and discover new time series forecasting methods early. I Follow myself on LinkedIn and Twitter to get regular updates on some of the most innovative technologies…

Generate accurate forecasts to understand how each prediction has been made. Machine learning for time series forecasting achieved significant successes during the last few years; machine learning methods dominated the leaderboard in the M5 Kaggle Walmart forecasting competition. And when I say machine learning, I mean precisely this — machine…

Leverage the full dataset efficiently for more precise probabilistic predictions Leverage the full dataset efficiently for more precise probabilistic predictions Conformal Prediction is a sizzling 🔥🔥🔥🔥🔥 area of research and application, garnering significant attention in both academia and industry. …

Working with forecasting and time series often requires evaluating time series similarities and differences. Whether one needs to cluster time series or compare forecasts with actual realisations, having robust similarity measures is useful when comparing time series data with different shapes and patterns. Whilst many metrics such as MAPE, MAE…

When analysing time series and building forecasting systems, it is essential to understand the predictive strength of endogenous and exogenous features and their effects on the target variable. Would it not be great to have some ranking of feature utility in terms of their predictive power? How could one achieve…