R package for online forecasting in various fields.
problem Frequent updates of forecasts in online settings.
method Generalized setup for time-adaptive fitting of dynamical and non-linear models.
result Effective use of forecasts as model inputs in operational settings.
ELF improves FM forecasts by efficiently using online feedback.
problem Fixed FMs fail to adapt to current data characteristics.
method ELF-Forecaster learns current data distribution, ELF-Weighter combines forecasts.
result ELF improves FM forecasts across various datasets.
Extends online linear regression to handle multivariate data.
problem Hierarchical forecasting with multivariate responses.
method Introduces MultiVAW, extending Vovk-Azoury-Warmuth algorithm to multivariate setting.
result Achieves logarithmic regret in time for multivariate online linear regression.
Survey of algorithms to correct past mistakes in prediction.
problem Improving prediction accuracy by correcting past errors.
method Defensive Forecasting as a sequential game theory approach to minimize prediction metrics.
result Simple, near-optimal algorithms for various prediction tasks.
Proceed adapts models proactively against concept drift in online time series forecasting.
problem Concept drift causes forecast models to adapt to outdated concepts, reducing performance.
method Proceed estimates and translates concept drift into parameter adjustments, enhancing model resilience.
result Proceed brings more performance improvements than state-of-the-art online learning methods.
A new framework detects forecast model inadequacies using online monitoring of forecast errors.
problem Inaccurate forecasts lead to poor decision-making in complex models.
method Sequential changepoint techniques on forecast errors for real-time identification of process changes.
result The framework identifies shifts in forecast errors faster than in the original models, indicating process changes.
BOA improves financial forecasting by combining expert models.
problem Challenges in choosing between multiple machine learning models for financial forecasting.
method Online aggregation of expert models using Bernstein Online Aggregation (BOA) procedure.
result BOA leads to better portfolio performance, higher Sharpe Ratio, and lower shortfall.
New algorithms reduce forecasting errors by leveraging optimistic learning and hinting.
problem Real-time climate forecasting with delayed feedback.
method Developed optimistic online learning algorithms that reduce regret under delayed feedback.
result Optimistic learning algorithms achieve optimal regret guarantees under delayed feedback.
Paper presents a method for probabilistic load forecasting using adaptive online learning.
problem Inability to assess intrinsic uncertainties and capture dynamic changes in consumption patterns.
method Adaptive online learning of hidden Markov models for recursive parameter updates and sequential prediction.
result Significant improvement in performance compared to existing techniques across various scenarios.
A winning method for day-ahead electricity demand forecasting during and after the COVID-19 pandemic.
problem Day-ahead electricity demand forecasting during and after the COVID-19 pandemic.
method Online forecast combination of multiple point prediction models with a holiday adjustment procedure and smoothed Bernstein Online Aggregation (BOA).
result Excellent forecasting performance, particularly due to the holiday adjustment procedure and fully adaptive smoothed BOA approach.
The problem of probabilistic forecasting and online simulation of real-time electricity market with stochastic generation and demand is considered. By exploiting the parametric structure of the direct current optimal power flow, a new technique based on online dictionary learning (ODL) is proposed. The ODL approach inc…
Paper improves probabilistic forecasts of electricity prices.
problem Combining multivariate probabilistic forecasts with dependencies.
method Online learning algorithm using Bernstein Online Aggregation (BOA).
result Significant improvement in CRPS over uniform combination.
OFTER predicts multivariate time series online, outperforming baselines.
problem Mid-sized multivariate time series forecasting challenges.
method k-nearest neighbors, Generalized Regression Neural Networks, dimensionality reduction.
result OFTER outperforms state-of-the-art baselines in financial multivariate time series forecasting.
New algorithms reduce label collection for online prediction with expert advice.
problem Efficiently predicting binary sequences with expert advice using fewer labels.
method Adaptive selective sampling for exponentially weighted forecasters.
result Label complexity scales roughly as the square root of the number of rounds for a scenario with a strictly better expert.
AutoML improves electricity demand forecasting models.
problem Optimizing GAM and state-space model parameters for short-term forecasting.
method Automated online generalized additive model selection using DRAGON package.
result The approach enhances predictive performance of adaptive models.
FSNet improves online time series forecasting by balancing fast adaptation and old knowledge.
problem Online time series forecasting challenges in handling abrupt and recurring patterns.
method Inspired by CLS theory, FSNet uses a dynamic balance between fast adaptation and old knowledge retrieval.
result FSNet achieves robustness to both new and recurring patterns through dynamic balancing and associative memory.
New method reduces forecasting error by up to 67% in various data types.
problem Outliers and model misspecification in online infinite hidden Markov models.
method Batched Robust iHMM (BR-iHMM) with bounded posterior influence function.
result Reduces one-step-ahead forecasting error by up to 67% in various data types.
New method improves forecast accuracy using CRPS for probabilistic predictions.
problem Improving forecast accuracy for probabilistic predictions, especially in the tails of distributions.
method Introduces a new weighting method for pointwise CRPS learning, optimizing across quantiles.
result Proposed fully adaptive Bernstein online aggregation (BOA) method for pointwise CRPS online learning has optimal convergence properties.
Online algorithm for probabilistic forecasting of conditional moments.
problem Probabilistic forecasting needs accurate learning of expected value and conditional heteroskedasticity.
method Combines online LASSO estimation with GAMLSS framework.
result Competitive performance in day-ahead electricity price forecasting.
In this paper we extend the setting of the online prediction with expert advice to function-valued forecasts. At each step of the online game several experts predict a function, and the learner has to efficiently aggregate these functional forecasts into a single forecast. We adapt basic mixable (and exponentially conc…
Adaptive volatility method improves probabilistic financial forecasting.
problem Probabilistic forecasting in financial markets.
method Adapts classical time-varying volatility models with online stochastic optimization.
result Ranked 5th in M6 financial forecasting competition.
New method achieves faster calibration without randomization.
problem Calibrating probabilistic forecasts in adversarial settings.
method Using interval forecasts and the power of two choices.
result Achieves O ( 1 / T ) O(1/T) O ( 1/ T ) calibration error rate without randomization. A new mechanism reduces expert belief regret in online forecasting.
problem Minimizing expert belief regret in strategic forecasting.
method Developed a no-regret mechanism for non-myopic experts using online I-ELF.
result Achieved i l d e O ( T N ) ilde{O}(\sqrt{T N}) i l d e O ( T N ) regret for full-information setting. Paper improves online time series forecasting by combining natural gradient and robust t-distribution.
problem Online time series forecasting challenges in rapidly adapting to evolving data.
method Reframed neural network optimization as a parameter filtering problem, using natural gradient and Student's t likelihood.
result Natural Score-driven Replay (NatSR) achieves stronger forecasting performance than state-of-the-art methods.
Paper adapts ACI for online multi-step time-series forecasting with coverage guarantees.
problem Achieving reliable error bounds in online multi-step time-series forecasting.
method Adaptive conformal inference (ACI) adapted for multi-step forecasting with dynamic significance levels.
result Proposes a multi-step ACI algorithm with finite-sample coverage guarantees for non-exchangeable data.
In this paper we introduce a novel online time series forecasting model we refer to as the pM-GP filter. We show that our model is equivalent to Gaussian process regression, with the advantage that both online forecasting and online learning of the hyper-parameters have a constant (rather than cubic) time complexity an…
Enhances load forecasting for multiple entities with dynamic similarities.
problem Inaccurate probabilistic load predictions due to uncertainties and dynamic changes.
method Online multi-task learning for probabilistic load forecasting.
result Significantly enhances load forecasting accuracy across various scenarios.
New framework for tracking varying bounds in time series forecasting.
problem Forecasting bounded time series with varying bounds.
method Extended log-likelihood estimation, online maximum likelihood estimation, Normalized Gradient Descent (NGD) for quasiconvex optimization.
result Derive an Online Normalized Gradient Descent algorithm for online bound tracking.
New online method for multivariate probabilistic electricity price forecasting.
problem Multivariate probabilistic forecasting of electricity prices.
method Online multivariate distributional regression with LASSO regularization.
result Robust and interpretable joint prediction intervals for 24-hour electricity prices.
Improved time series forecasting with expert loss integration.
problem Enhancing time series forecasting accuracy and efficiency.
method Adaptive Mixture-of-Experts framework with expert-specific loss integration and online learning.
result Significantly improved forecasting accuracy and computational efficiency.
Paper addresses CPA line forecasting in online advertising mid-flight.
problem Forecasting ad campaign performance mid-flight considering bidding mechanisms.
method Generates relationships between metrics and optimization signals, estimates sensitivity, and characterizes advertiser spends vs. eCPA.
result Demonstrates promising accuracy in forecasting against actual deliveries.
Develops a method to ensure accurate quantile forecasts across multiple levels.
problem Ensuring accurate quantile forecasts at multiple levels, even under distribution shifts.
method Multi-level quantile tracker (MultiQT) wraps around any forecaster to produce calibrated forecasts.
result Guaranteed calibration of quantile forecasts at multiple levels, even against adversarial shifts.
OneShotSTL efficiently decomposes time series online, improving speed and accuracy.
problem Real-time analysis of time series data with low processing delay.
method Online seasonal-trend decomposition algorithm with O(1) update time complexity.
result 1,000 times faster than batch methods with comparable accuracy.
We consider an online learning process to forecast a sequence of outcomes for nonconvex models. A typical measure to evaluate online learning algorithms is regret but such standard definition of regret is intractable for nonconvex models even in offline settings. Hence, gradient based definition of regrets are common f…
A new method calibrates forecasts without sacrificing expertise.
problem Forecasters' calibration scores can be manipulated to appear expert.
method Deterministic and stochastic online procedures to calibrate forecasts.
result Calibration can be achieved without losing expertise.
Optimal online linear regression in dynamic environments using discounted Vovk-Azoury-Warmuth forecaster.
problem Achieving optimal performance in dynamic online linear regression without prior knowledge.
method Developed a discounted variant of the Vovk-Azoury-Warmuth forecaster to achieve optimal dynamic regret guarantees.
result Achieved dynamic regret of the form $O\left(d\log(T)\vee \sqrt{dP_{T}^γ(\vec{u})T}
ight)$ , with a learnable discount factor.
Online data augmentation improves forecasting performance in deep learning.
problem Insufficient training data for forecasting tasks.
method An online data augmentation framework that generates synthetic samples during training.
result Online data augmentation leads to better forecasting performance.
Kernel interpolation speeds up online Gaussian process updates.
problem Efficiently updating Gaussian process posteriors with new data.
method Structured kernel interpolation for constant-time updates.
result Exact inference maintained with constant-time updates.
Deep learning model reduces food waste by stabilizing online food delivery supply chains.
problem Wastage and bullwhip effect in online food delivery services.
method Two-phase LSTM network for demand forecasting, newsvendor model for inventory management.
result Significant reduction in bullwhip effect and food waste, improved forecasting accuracy.
Online learning rbfnet improves multi-horizon returns forecasts for financial time series.
problem Nonstationarity and concept drift in financial time series.
method Combines feature representation transfer with sequential optimisation.
result Online learning rbfnet outperforms random-walk and batch learners.
Paper compares two forecasters using novel online inference methods.
problem How to compare forecasters without distributional assumptions.
method Confidence sequences and game-theoretic statistical framework for sequential testing.
result Valid methods for comparing forecasters without distributional assumptions.
Smooth calibration improves forecast reliability even with leaked information.
problem Improving forecast reliability with leaked information.
method Combining nearby forecasts to ensure smooth calibration, which can be guaranteed by deterministic procedures.
result Smooth calibration can be guaranteed by deterministic procedures even with leaked forecasts, and it yields uncoupled finite-memory dynamics in games.
Gradient-free ensemble learns sector forecasts from diverse models.
problem Predicting sector returns in a volatile market.
method Dynamic model combination using out-of-sample R-squared.
result Ensemble outperforms individual models in sector rotation.
New algorithms improve time series prediction with uncertainty quantification.
problem Uncertainty quantification for time series prediction.
method Combines conformal prediction and control theory for online, adaptive forecasting.
result Improves coverage over ensemble forecasters in real-world applications.
Proposes a compensation mechanism for improving individual forecast confidence.
problem Difficult to assess the quality of individual probabilistic forecasts and their utilities.
method Compensation mechanism based on fair bets and online learning.
result The proposed mechanism cannot be exploited and ensures forecasted utility matches actual utility.
Adaptive probabilistic load forecasting improves performance in power systems.
problem Complexity of electricity load forecasting due to changing drivers and local generation.
method Adaptive probabilistic approach using Kalman filter and online gradient descent.
result Adaptive probabilistic forecasts improve performance in both point and probabilistic forecasting.
We establish optimal rates for online regression for arbitrary classes of regression functions in terms of the sequential entropy introduced in (Rakhlin, Sridharan, Tewari, 2010). The optimal rates are shown to exhibit a phase transition analogous to the i.i.d./statistical learning case, studied in (Rakhlin, Sridharan,…
Hybrid model improves weather forecasting accuracy.
problem Combining physics and machine learning for better weather predictions.
method Offline and online training of a neural network to correct model errors in the IFS.
result The hybrid model reduces forecast errors and improves accuracy.