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problem Concept drift causes forecast models to adapt to outdated concepts, reducing performance.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.
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.
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.
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.
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 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.
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.
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 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.
New method forecasts time series with changing variances.
problem Real-world processes with changing variances cannot be captured by classical models.
method State-space model with Markov switching variances, using online learning and expert aggregation.
result Proposed method outperforms traditional expert aggregation and is robust to misspecification.
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.
Improved online penalty selection for time series models.
problem Efficiently selecting penalty parameters for lasso in time series models.
method Enhanced autoregressive model with online penalty selection.
result Significantly improved computational performance and forecast accuracy.
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.
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…
SPADE-S improves time series forecasting accuracy for low-magnitude and sparse data.
problem Challenges in forecasting time series with strong heterogeneity in magnitude and sparsity.
method SPADE-S is a robust forecasting architecture that reduces biases and improves overall prediction accuracy.
result SPADE-S outperforms existing state-of-the-art approaches across diverse use cases, improving forecast accuracy by up to 15%.
POLA adapts learning rates for online time series prediction.
problem Adapting to changing data distributions in dynamic environments.
method Adaptive learning rate regulation for recurrent neural networks.
result POLA outperforms other online prediction methods in real-world datasets.
VEST automates feature engineering for time series forecasting.
problem Challenges in time series forecasting with improved performance.
method VEST combines auto-regression with statistical summarization of recent past dynamics.
result VEST significantly improves forecasting performance.
New approach improves convergence of online learning for ARIMA models.
problem Pitfalls in computational complexity and convergence of Online Newton Step optimization method.
method Proposes a new approach combining different Online Gradient Descent learners for iterative training of ARIMA models.
result The proposed approach outperforms existing methods resulting in lower prediction error.
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.
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.
A training-free conformal interval is a mandatory baseline for probabilistic time-series forecasting.
problem Comparing probabilistic forecasters against weak or omitted baselines.
method A simple conformal interval with no parameters and no training.
result The ConformalNaive interval decisively beats several baselines.
Adaptive Conformal Inference improves time series forecasting uncertainty.
problem Uncertainty quantification in time series models with dependency.
method AgACI, an adaptive method based on online expert aggregation.
result AgACI provides efficient prediction intervals for day-ahead electricity price forecasting.
Warped DLMs improve forecasting for count time series.
problem Limited options for modeling count time series data.
method Introduces a semiparametric methodology using warping of Gaussian DLMs.
result Demonstrates improved forecasting capabilities for count time series.
Ensemble of MFESNs improves macroeconomic forecasting.
problem Improving accuracy in macroeconomic forecasting.
method Hedge and Follow-the-Leader schemes applied to MFESNs.
result Ensemble models outperform individual models in forecasting.
A RL approach dynamically assigns and updates weights of ensemble models for better time series forecasting.
problem Static weight assignment for ensemble models fails to capture dynamic data changes.
method Reinforcement Learning (RL) to dynamically update weights of each model at different time instants.
result Dynamic weighted approach using RL learns weights better than static methods.
EvoMSN tackles time series forecasting under distribution shifts by evolving multi-scale normalization.
problem Accurate long-term time series forecasting under complex distribution shifts.
method EvoMSN framework with multi-scale statistics prediction and adaptive ensembling for collaborative updating.
result Improves forecasting performance of five mainstream methods on benchmark datasets.
New algorithm reduces TV-denoising to adaptive online learning.
problem Estimating TV-bounded functions from noisy samples.
method Deep connection to Strongly Adaptive online learning; O(nlogn) time algorithm. result Near minimax optimal rate of O(n1/3Cn2/3) under squared error loss. POSL is an online learning algorithm for personalized predictions.
problem Real-time personalized predictions for streaming data.
method Online Super Learner algorithm that optimizes predictions with respect to baseline covariates.
result POSL provides reliable predictions and adapts to changing data environments.
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 paper develops a method for forecasting power consumption at various levels of aggregation.
problem Forecasting power consumption at different levels of household aggregation.
method Three-step process: feature generation, aggregation, and projection.
result The method provides theoretical guarantees on prediction error and performs well on real data.
MoE-F combines LLMs online for better time-series prediction.
problem Combining multiple LLMs for online time-series prediction.
method Time-adaptive stochastic filtering techniques to combine experts.
result MoE-F achieves 17% absolute and 48.5% relative F1 measure improvement.
We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting. The framework can be applied to estimate probability density under both parametric and non-parametric settings. More specifically, stacked residual blocks based on dilated causal convolut…
Time series forecasting is widely used in a multitude of domains. In this paper, we present four models to predict the stock price using the SPX index as input time series data. The martingale and ordinary linear models require the strongest assumption in stationarity which we use as baseline models. The generalized li…
BC-ACI corrects time series forecast bias, improving prediction intervals.
problem Persistent bias in time series forecasts leads to overly conservative prediction intervals.
method Augments ACI with an EWM estimate of forecast bias to correct nonconformity scores and re-center intervals.
result Reduces Winkler interval scores by 13-17% under distribution shifts, improving calibration.
For the prediction with experts' advice setting, we construct forecasting algorithms that suffer loss not much more than any expert in the pool. In contrast to the standard approach, we investigate the case of long-term forecasting of time series and consider two scenarios. In the first one, at each step t the learne…
Variant of mSSA improves time series prediction error.
problem Improve prediction error in multivariate time series.
method Introduce spatio-temporal factor model, establish prediction error scaling.
result Prediction error scales as 1 / √(min(N, T)T).
Bayesian Context Trees model improves financial time series forecasting.
problem Modeling and forecasting financial time series with volatility asymmetries.
method Hierarchical Bayesian framework for tree-based mixture models with AR/ARCH base models.
result BCT-X framework outperforms state-of-the-art techniques in forecasting accuracy and computational efficiency.
Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series …
Archive of 20 time series datasets for forecasting evaluation.
problem Lack of comprehensive time series forecasting datasets.
method Compilation and characterisation of 20 datasets from various domains.
result Characterisation and performance evaluation of datasets.
Paper proposes using online text data to predict CPI with LLMs.
problem Forecasting Consumer Price Index (CPI) using low-frequency survey-based data.
method Develops an LLM-based approach combining online text time series with monthly CPI data.
result Establishes the asymptotic properties and provides prediction intervals for CPI forecasts.
Online learning improves traffic congestion prediction over time.
problem Model degradation due to concept drift in traffic data.
method Incremental learning from non-stationary time series data.
result Performance of models degrades with increased prediction horizon.
For2For combines forecasts to improve time series forecasting.
problem Improving time series forecasting accuracy.
method Combines standard forecasting methods and machine learning models using forecasts as features.
result Outperforms all submissions in the M4 competition for quarterly series and most monthly series.
New method uses Transformers for flu forecasting.
problem Forecasting influenza-like illness trends.
method Transformer-based machine learning models with self-attention.
result Forecasting results are competitive with state-of-the-art methods.
New method discovers accurate time series models using SMC and MCMC.
problem Discovering accurate models of complex time series data.
method Bayesian nonparametric prior, sequential Monte Carlo (SMC), involutive MCMC.
result 10x--100x runtime speedup over previous methods.
Few-shot learning improves time-series forecasting with limited data.
problem Limited data in target tasks degrade forecasting performance.
method A few-shot learning method using recurrent neural networks with attention.
result The model forecasts future values effectively with minimal data.
EasyTime simplifies time series forecasting for researchers and practitioners.
problem Ease of use and accuracy in time series forecasting.
method One-click evaluation, automated ensemble, natural language Q&A.
result Superior forecasting accuracy compared to individual methods.
ABO extends RLS for online learning in non-stationary time-series, improving accuracy and speed.
problem Online learning in non-stationary time-series with overparameterized models.
method QR-based exponentially weighted RLS algorithm with orthogonal-triangular updates.
result ABO maintains bounded residuals and stable condition numbers while achieving speed improvements.
This paper improves forecasts for diverse time series by averaging similar ones.
problem Forecasting challenges in heterogeneous time series.
method Dynamic Time Warping to find similar time series, k-Nearest Neighbor averaging.
result Averaging improves forecasts of simple models.