DeepAI AI Chat
Log In Sign Up

Adaptive Conformal Predictions for Time Series

by   Margaux Zaffran, et al.

Uncertainty quantification of predictive models is crucial in decision-making problems. Conformal prediction is a general and theoretically sound answer. However, it requires exchangeable data, excluding time series. While recent works tackled this issue, we argue that Adaptive Conformal Inference (ACI, Gibbs and Candès, 2021), developed for distribution-shift time series, is a good procedure for time series with general dependency. We theoretically analyse the impact of the learning rate on its efficiency in the exchangeable and auto-regressive case. We propose a parameter-free method, AgACI, that adaptively builds upon ACI based on online expert aggregation. We lead extensive fair simulations against competing methods that advocate for ACI's use in time series. We conduct a real case study: electricity price forecasting. The proposed aggregation algorithm provides efficient prediction intervals for day-ahead forecasting. All the code and data to reproduce the experiments is made available.


page 1

page 2

page 3

page 4


A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting

The exponential growth of machine learning (ML) has prompted a great dea...

Copula Conformal Prediction for Multi-step Time Series Forecasting

Accurate uncertainty measurement is a key step to building robust and re...

Conformal PID Control for Time Series Prediction

We study the problem of uncertainty quantification for time series predi...

Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models

We focus on day-ahead electricity load forecasting of substations of the...

Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting

We present a winning method of the IEEE DataPort Competition on Day-Ahea...

Reducing climate risk in energy system planning: a posteriori time series aggregation for models with storage

The growth in variable renewables such as solar and wind is increasing t...

Better Batch for Deep Probabilistic Time Series Forecasting

Deep probabilistic time series forecasting has gained significant attent...