Melanie improves predictive performance in non-stationary data streams by transferring knowledge between multiple sources.
problem Concept drift in data streams leads to poor predictive performance.
method Melanie uses multiple sub-classifiers to learn different aspects from various sources and compose an ensemble for the target concept.
result Melanie improves predictive performance over existing algorithms by leveraging multiple sources.
A new method for predicting uncertainties in stream networks.
problem Uncertainty quantification in spatiotemporal graphs with directional flow constraints.
method Spatio-Temporal Adaptive Conformal Inference (STACI) integrating network topology and temporal dynamics.
result STACI effectively balances prediction efficiency and coverage, outperforming existing methods.
Proposes a multi-stream RNN model for predicting merchant transactions.
problem Predicting future transaction statistics of merchants.
method Multi-stream RNN model tailored for multivariate time series and multi-step predictions.
result Outperforms existing state-of-the-art methods in merchant transaction predictions.
HyperStream processes streaming data with workflow creation.
problem Processing large-scale, real-time data challenges.
method Python-based workflow engine for flexible, robust data processing.
result Overcomes limitations of other computational engines.
Quadratic memory needed for linear prediction in streaming model.
problem Scalable memory-efficient linear prediction in streaming model.
method Memory lower bound for finding orthogonal vectors and estimates on Grassmannian packing.
result Problems cannot be solved by scalable memory-efficient streaming algorithms.
StreamEnsemble dynamically selects ML models for ST data streams to improve predictive accuracy.
problem Predictive queries over spatiotemporal data streams are challenging due to varying distributions and patterns.
method Dynamic selection and allocation of ML models based on time series distributions and characteristics.
result Significantly outperforms traditional ensemble and single model approaches, reducing prediction error by over 10 times.
Non-parametric method predicts multi-stream longitudinal data evolution.
problem Predicting the evolution of multi-stream longitudinal data for an in-service unit.
method Decomposes each stream into eigenfunctions and FPC scores, uses Gaussian process prior and empirical Bayesian updating.
result Framework outperforms state-of-the-art approaches and achieves high predictive accuracy.
VQ-BNN speeds up BNN inference for data streams.
problem High computational cost of BNN inference for data streams.
method Approximates BNN inference by predicting NN only once and using temporal exponential smoothing of recent predictions.
result VQ-BNN performs faster than BNNs while estimating comparable results.
New framework improves fraud prediction with incremental data balancing for massive data streams.
problem Class imbalance problem in massive imbalanced data streams.
method Incremental data balancing framework using Racing Algorithm for automated balancing and Random Forest for classification.
result Better results than Batch mode on European Credit Card dataset.
LOBDIF predicts limit order book events using a diffusion model.
problem Predicting the timing and type of events in a dynamic market system.
method LOBDIF uses a diffusion model to learn the complex time-event distribution in limit order book streams.
result LOBDIF significantly outperforms existing methods in real-world data experiments.
New algorithm detects and handles concept drift in data streams.
problem Handling concept drift in data streams for timely predictions.
method Hybrid Forest algorithm combining Hoeffding Trees and fast startup.
result The algorithm outperforms other methods in classification and regression tasks.
A new framework predicts hidden Markov model regimes online.
problem Efficiently identify hidden Markov model regimes in streaming data.
method Develops a predictive-first optimisation framework for streaming HMMs, approximating the full posterior predictive distribution.
result The method provides competitive prequential performance compared to Online EM and Sequential Monte Carlo.
Convolutional neural networks outperform other architectures in streaming time series classification.
problem Efficient deep learning models for real-time data streams.
method Asynchronous dual-pipeline deep learning framework for real-time predictions.
result Convolutional architectures achieve higher accuracy and efficiency in streaming time series classification.
Unified study of stateful replay for streaming learning, reducing forgetting by 2-3x.
problem Catastrophic forgetting in streaming generative and predictive learning.
method Unified analysis of stateful replay for autoencoding, forecasting, and classification tasks.
result Stateful replay reduces average forgetting by a factor of 2-3 on heterogeneous multi-task streams.
Proposes a new online learning strategy for multi-target regression in data streams.
problem Challenges in learning from high-throughput data streams, especially in multi-target regression.
method Extends existing online decision tree learning algorithm to consider inter-target dependencies.
result SST-HT presents superior predictive accuracy compared to state-of-the-art algorithms.
stream-learn is a Python library for analyzing data streams with various drift types.
problem Analyzing drifting and imbalanced data streams.
method Synthetic data stream generator, evaluation methodologies, and imbalanced binary classification metrics.
result Efficient implementation of classifiers for data stream analysis.
New robustness certificates for streaming models with a sliding window.
problem Applying robustness certificates to streaming data with correlated inputs.
method Deriving robustness certificates for models using a sliding window over a sequence of potentially correlated inputs.
result Guarantees hold for the average model performance across the entire stream, independent of stream size.
Dynamic Model Tree improves online learning for evolving data streams.
problem Effective and transparent machine learning on data streams is challenging.
method Revisit Model Trees for data stream applications, introducing Dynamic Model Tree.
result Dynamic Model Tree reduces the number of splits and outperforms state-of-the-art models.
Visual analytics tool detects and corrects concept drift in data streams.
problem Concept drift causes inaccurate predictions in evolving data.
method DriftVis combines drift detection and visualization.
result Visual analytics supports detection, examination, and correction of concept drift.
Optimizes deep reinforcement learning for energy-efficient video streaming.
problem Minimizing energy consumption in video streaming over mobile networks.
method Integrates DDPG algorithm with partially known model to reduce signaling overhead and improve convergence speed.
result Proposed policy converges to optimal policy with improved convergence speed.
OLBoost improves online decision tree performance without increasing memory or time costs.
problem Improving predictive performance in online decision trees without high memory or time costs.
method OLBoost applies boosting to small regions of the instances space within online decision tree algorithms.
result OLBoost can significantly improve online learning decision tree performance without increasing tree size.
An important metric of users' satisfaction and engagement within on-line streaming services is the user session length, i.e. the amount of time they spend on a service continuously without interruption. Being able to predict this value directly benefits the recommendation and ad pacing contexts in music and video strea…
Develops anytime-valid conformal and PAC prediction for streaming data.
problem Lack of guarantee in traditional conformal methods for sequential settings.
method Extends conformal and PAC prediction frameworks to handle streaming data.
result Provides anytime-valid prediction sets for sequential settings.
DDG-DA predicts future data distribution to adapt models for predictable concept drift.
problem Adapting models to streaming data with predictable concept drift.
method Train a predictor to forecast future data distribution, generate training samples, and train models on them.
result Significant improvement on multiple models in real-world tasks.
Improved tracking and prediction of moving objects in visual data streams.
problem Tracking and predicting multiple moving objects in visual data streams.
method Disentangled latent state-space model with amortized variational Bayesian inference.
result Significantly improved long-term prediction and object decomposition in the presence of occlusions.
Modeling wildfire aerosols using satellite data to predict solar radiation reduction.
problem Accurately estimate and predict AOD propagation from wildfires using multi-source satellite data.
method Physics-informed statistical modeling integrating multi-source satellite data with an advection-diffusion equation.
result The proposed approach accurately predicts AOD propagation and demonstrates model interpretability.
Study uses deep neural networks for flood forecasting.
problem Accurate flood predictions everywhere.
method Artificial deep neural networks for time-series forecasting.
result Neural networks improve flood predictions.
We bring the theory of rough paths to the study of non-parametric statistics on streamed data. We discuss the problem of regression where the input variable is a stream of information, and the dependent response is also (potentially) a stream. A certain graded feature set of a stream, known in the rough path literature…
This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture. This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitr…
In many machine learning applications, it is important to explain the predictions of a black-box classifier. For example, why does a deep neural network assign an image to a particular class? We cast interpretability of black-box classifiers as a combinatorial maximization problem and propose an efficient streaming alg…
Stream mining poses unique challenges to machine learning: predictive models are required to be scalable, incrementally trainable, must remain bounded in size (even when the data stream is arbitrarily long), and be nonparametric in order to achieve high accuracy even in complex and dynamic environments. Moreover, the l…
Proposes a model for predicting events from event streams.
problem Predicting events like part replacement and failure in manufacturing and teleservice systems.
method Non-parametric prognostic framework using MGCP modulated Poisson processes.
result MGCP prior facilitates sharing of information and analysis of flexible event patterns.
Two new approaches for point prediction in streaming data, showing consistency and performance.
problem Predicting points in streaming data without a true model.
method Count-Min sketch and Gaussian process priors with random bias.
result CMS-based estimates are consistent under i.i.d. samples assumption.
Streaming adaptations of manifold learning based dimensionality reduction methods, such as Isomap, are based on the assumption that a small initial batch of observations is enough for exact learning of the manifold, while remaining streaming data instances can be cheaply mapped to this manifold. However, there are no t…
Paper proposes an online sparse linear regression method for streaming data.
problem Sparse regression for variable selection and prediction accuracy.
method Online sparse linear regression framework with memory efficiency and relaxed assumptions.
result The ℓ2-norm statistical error of the estimator diminishes to zero with optimal order. Recognising human activities from streaming videos poses unique challenges to learning algorithms: predictive models need to be scalable, incrementally trainable, and must remain bounded in size even when the data stream is arbitrarily long. Furthermore, as parameter tuning is problematic in a streaming setting, suitab…
Proposes DTS framework to predict CTR by tracking user interest evolution over time.
problem Predicting CTR by ignoring dynamic user interest changes over time.
method Integrates time information using ODEs in a neural network to model interest evolution.
result Achieves superior CTR prediction performance compared to existing methods.
New algorithm accelerates single-pass SGD for generalized linear prediction.
problem Improving single-pass non-quadratic stochastic optimization.
method Data-dependent proximal method incorporating dual-momentum acceleration.
result Momentum acceleration resolves open problem in streaming setting.
Anomalies in time-series data give essential and often actionable information in many applications. In this paper we consider a model-free anomaly detection method for univariate time-series which adapts to non-stationarity in the data stream and provides probabilistic abnormality scores based on the conformal predicti…
LINTEL improves INTEL's time series prediction by optimizing computation and accuracy.
problem Online prediction of time series with regime switching and outliers.
method Gaussian process-based approach with exact filtering distribution and constant-time updates.
result LINTEL is over five times faster with better quality predictions.
A new framework detects concept drift in streaming data.
problem Detecting distributional changes in non-stationary data streams.
method Treating model parameters as random variables, ERICS uses information theory measures to identify concept drift.
result ERICS effectively detects concept drift compared to existing methods.
Dual frame-rate system improves real-time perception for autonomous vehicles.
problem Conflicting requirement of safety and efficiency in real-time perception systems.
method Proposes a dual frame-rate system with a modulator stream for robust features and a prediction stream for transient signals.
result Consistent improvements across various backbone architectures and input resolutions.
This paper optimizes resource allocation for crowdsourced live streaming to improve viewer experience and reduce costs.
problem Improving viewer quality of experience (QoE) in crowdsourced live streaming.
method A prediction-driven resource allocation framework using machine learning to predict viewer numbers and proactively allocate resources.
result Maximizes viewer QoE and minimizes resource allocation costs through precise resource provisioning.
A federated model predicts failures using multi-stream incomplete data.
problem Insufficient data for reliable prognostic models in multi-stream applications.
method Federated data fusion, multivariate functional principal component analysis, (log)-location-scale regression model, federated algorithm.
result Performance is as good as classic non-federated models and better than individual models.
New method detects when models influence their own drift in real-time data streams.
problem Models can induce concept drift in real-time data streams.
method CheckerBoard Performative Drift Detection (CB-PDD)
result CB-PDD effectively detects performative drift in real-time data streams.
CURIE uses cellular automata to detect concept drift in data streams.
problem Detecting changes in data distribution (concept drift) in data streams.
method CURIE represents data stream distribution in a cellular automata grid and uses its neighborhood rule to detect changes.
result CURIE, when hybridized with base learners, performs competitively in detection metrics and classification accuracy.
TM-CNN predicts lane-level traffic speeds considering volume impact.
problem Aggregated lane-level traffic speed prediction and volume impact.
method Two-stream multi-channel CNN, data conversion, two-stream deep neural network, loss function.
result TM-CNN outperforms existing models in multi-lane traffic speed prediction.
Proposes methods to make data streams fair without fixing a model.
problem Fairness of data-driven models in evolving data streams.
method Modifies input data to ensure fair outcomes for any classifier.
result Improves predictive performance and low discrimination scores over time.