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Found 10 papers, 0 papers with code
Adaptive time series forecasting with markovian variance switching
In this paper, we propose a new way of estimating variances based on online learning theory; we adapt expert aggregation methods to learn the variances over time.
Online Learning Approach for Survival Analysis
We introduce an online mathematical framework for survival analysis, allowing real time adaptation to dynamic environments and censored data.
An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition
In this note, we address the problem of probabilistic forecasting using an adaptive volatility method based on classical time-varying volatility models and stochastic optimization algorithms.
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models
Moreover, we are interested in forecasting the loads of over one thousand substations; consequently, we are in the context of forecasting multiple time series.
Adaptive Probabilistic Forecasting of Electricity (Net-)Load
The proliferation of local generation, demand response, and electrification of heat and transport are changing the fundamental drivers of electricity load and increasing the complexity of load modelling and forecasting.
State-Space Models Win the IEEE DataPort Competition on Post-covid Day-ahead Electricity Load Forecasting
On the one hand, purely time-series models such as autoregressives are adaptive in essence but fail to capture dependence to exogenous variables.
Viking: Variational Bayesian Variance Tracking
We introduce an augmented model in which the variances are represented as auxiliary gaussian latent variables in a tracking mode.
Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France
One of the consequences of this policy is a significant change in electricity consumption patterns.
Stochastic Online Optimization using Kalman Recursion
Second, for generalized linear regressions, we provide a martingale analysis of the excess risk in the local phase, improving existing ones in bounded stochastic optimization.
Logarithmic Regret for parameter-free Online Logistic Regression
We consider online optimization procedures in the context of logistic regression, focusing on the Extended Kalman Filter (EKF).
