Act Now: A Novel Online Forecasting Framework for Large-Scale Streaming Data

• URL: http://arxiv.org/abs/2412.00108v1
• Date: Thu, 28 Nov 2024 01:39:45 GMT
• Title: Act Now: A Novel Online Forecasting Framework for Large-Scale Streaming Data
• Authors: Daojun Liang, Haixia Zhang, Jing Wang, Dongfeng Yuan, Minggao Zhang,
• Abstract summary: Existing online forecasting methods have the following issues. They do not consider the update frequency of streaming data. Eliminating information leakage can exacerbate concept drift. Existing GPU devices cannot support online learning of large-scale streaming data.
• Score: 17.121851529311368
• License:
• Abstract: In this paper, we find that existing online forecasting methods have the following issues: 1) They do not consider the update frequency of streaming data and directly use labels (future signals) to update the model, leading to information leakage. 2) Eliminating information leakage can exacerbate concept drift and online parameter updates can damage prediction accuracy. 3) Leaving out a validation set cuts off the model's continued learning. 4) Existing GPU devices cannot support online learning of large-scale streaming data. To address the above issues, we propose a novel online learning framework, Act-Now, to improve the online prediction on large-scale streaming data. Firstly, we introduce a Random Subgraph Sampling (RSS) algorithm designed to enable efficient model training. Then, we design a Fast Stream Buffer (FSB) and a Slow Stream Buffer (SSB) to update the model online. FSB updates the model immediately with the consistent pseudo- and partial labels to avoid information leakage. SSB updates the model in parallel using complete labels from earlier times. Further, to address concept drift, we propose a Label Decomposition model (Lade) with statistical and normalization flows. Lade forecasts both the statistical variations and the normalized future values of the data, integrating them through a combiner to produce the final predictions. Finally, we propose to perform online updates on the validation set to ensure the consistency of model learning on streaming data. Extensive experiments demonstrate that the proposed Act-Now framework performs well on large-scale streaming data, with an average 28.4% and 19.5% performance improvement, respectively. Experiments can be reproduced via https://github.com/Anoise/Act-Now.

Related papers

• Online-BLS: An Accurate and Efficient Online Broad Learning System for Data Stream Classification [52.251569042852815]
  We introduce an online broad learning system framework with closed-form solutions for each online update. We design an effective weight estimation algorithm and an efficient online updating strategy. Our framework is naturally extended to data stream scenarios with concept drift and exceeds state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-01-28T13:21:59Z)
• Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting [23.50574069148193]
  We present a novel proactive model adaptation framework for online time series forecasting. Proceed first estimates the concept drift between the recently used training samples and the current test sample. It then employs an adaptation generator to efficiently translate the estimated drift into parameter adjustments.
  arXiv  Detail & Related papers  (2024-12-11T14:57:10Z)
• Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt [37.98336090671441]
  Concept textbfDrift textbfDetection antextbfD textbfAdaptation (D3A) It first detects drifting conception and then aggressively adapts the current model to the drifted concepts after the detection for rapid adaption. It helps mitigate the data distribution gap, a critical factor contributing to train-test performance inconsistency.
  arXiv  Detail & Related papers  (2024-03-22T04:44:43Z)
• OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling [65.93805881841119]
  We propose textbfOnline textbfensembling textbfNetwork (OneNet) to address the concept drifting problem. OneNet reduces online forecasting error by more than $mathbf50%$ compared to the State-Of-The-Art (SOTA) method.
  arXiv  Detail & Related papers  (2023-09-22T06:59:14Z)
• Semi-Autoregressive Streaming ASR With Label Context [70.76222767090638]
  We propose a streaming "semi-autoregressive" ASR model that incorporates the labels emitted in previous blocks as additional context. Experiments show that our method outperforms the existing streaming NAR model by 19% relative on Tedlium2, 16%/8% on Librispeech-100 clean/other test sets, and 19%/8% on the Switchboard(SWB)/Callhome(CH) test sets.
  arXiv  Detail & Related papers  (2023-09-19T20:55:58Z)
• Sampling Streaming Data with Parallel Vector Quantization -- PVQ [0.0]
  We present a vector quantization-based sampling method, which substantially reduces the class imbalance in data streams. We built models using parallel processing, batch processing, and randomly selecting samples. We show that the accuracy of classification models improves when the data streams are pre-processed with our method.
  arXiv  Detail & Related papers  (2022-10-04T17:59:44Z)
• StreamYOLO: Real-time Object Detection for Streaming Perception [84.2559631820007]
  We endow the models with the capacity of predicting the future, significantly improving the results for streaming perception. We consider multiple velocities driving scene and propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy. Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively.
  arXiv  Detail & Related papers  (2022-07-21T12:03:02Z)
• Real-time Object Detection for Streaming Perception [84.2559631820007]
  Streaming perception is proposed to jointly evaluate the latency and accuracy into a single metric for video online perception. We build a simple and effective framework for streaming perception. Our method achieves competitive performance on Argoverse-HD dataset and improves the AP by 4.9% compared to the strong baseline.
  arXiv  Detail & Related papers  (2022-03-23T11:33:27Z)
• A Broad Ensemble Learning System for Drifting Stream Classification [3.087840197124265]
  We propose a Broad Ensemble Learning System (BELS) for stream classification with concept drift. BELS uses a novel updating method that greatly improves best-in-class model accuracy. We show that our proposed method improves on average 44% compared to BLS, and 29% compared to other competitive baselines.
  arXiv  Detail & Related papers  (2021-10-07T15:01:33Z)
• Over-the-Air Federated Learning from Heterogeneous Data [107.05618009955094]
  Federated learning (FL) is a framework for distributed learning of centralized models. We develop a Convergent OTA FL (COTAF) algorithm which enhances the common local gradient descent (SGD) FL algorithm. We numerically show that the precoding induced by COTAF notably improves the convergence rate and the accuracy of models trained via OTA FL.
  arXiv  Detail & Related papers  (2020-09-27T08:28:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.