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Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case

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arxiv 2001.08317 v1 pith:QHFERS3H submitted 2020-01-23 cs.LG stat.ML

Neo Wu , Bradley Green , Xue Ben , Shawn O'Banion This is my paper · ORCID
classification cs.LGstat.ML
keywords seriestimedataforecastingapproachcaselearningmachine
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In this paper, we present a new approach to time series forecasting. Time series data are prevalent in many scientific and engineering disciplines. Time series forecasting is a crucial task in modeling time series data, and is an important area of machine learning. In this work we developed a novel method that employs Transformer-based machine learning models to forecast time series data. This approach works by leveraging self-attention mechanisms to learn complex patterns and dynamics from time series data. Moreover, it is a generic framework and can be applied to univariate and multivariate time series data, as well as time series embeddings. Using influenza-like illness (ILI) forecasting as a case study, we show that the forecasting results produced by our approach are favorably comparable to the state-of-the-art.

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Cited by 14 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Transformers—especially a standard encoder-decoder—yield the lowest hourly load forecast errors across TSO, low-voltage feeder, and client-level datasets, with 6.6–10.7% error reduction over the best non-Transformer baseline.

  2. NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts

    cs.LG 2026-07 conditional novelty 6.0 of 10

    NEST improves long-term multivariate forecasting under dataset-level distribution shifts by clustering regimes in moment-entropy space and recomposing specialized variate-attention experts via a content-plus-geometry router.

  3. Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection

    cs.AI 2025-07 conditional novelty 6.0 of 10

    REP-Net, a modular pipeline of representation, memory, and projection modules, achieves competitive forecasting accuracy on seven multivariate benchmarks with lower computational cost.

  4. E-STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting

    stat.AP 2024-11 conditional novelty 6.0 of 10

    A spatiotemporal graph network with a generalized Pareto loss is introduced and shown to outperform most benchmarks for Delhi PM2.5, PM10, and NO2 forecasting.

  5. A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility

    physics.acc-ph 2026-07 conditional novelty 5.5 of 10

    A deployed hybrid RAG for APS operations improves vital-nugget recall over BM25 mainly via cross-encoder reranking; graph and corrective loops help only marginally on a 50-question facility benchmark.

  6. Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models

    cs.LG 2026-08 conditional novelty 5.0 of 10

    Reward-based fine-tuning of time series foundation models can collapse predictions away from the true future; steering probability mass into a ground-truth neighborhood reduces that collapse and improves forecasts.

  7. Evaluating the Performance of Deep Learning Models in Whole-body Dynamic 3D Posture Prediction During Load-reaching Activities

    cs.CV 2025-11 conditional novelty 5.0 of 10

    Transformer networks with a segment-length-constraining loss predict whole-body 3D posture during load-reaching with ~41 mm RMSE, beating BLSTM on long-horizon recursive prediction.

  8. TSRM: A Lightweight Temporal Feature Encoding Architecture for Time Series Forecasting and Imputation

    cs.LG 2025-04 conditional novelty 5.0 of 10

    A compact CNN-plus-attention architecture, TSRM, is reported to beat or match state-of-the-art models on most forecasting and imputation benchmarks with far fewer parameters.

  9. Predicting Human Brain States with Transformer

    q-bio.NC 2024-12 conditional novelty 5.0 of 10

    A transformer predicts the next fMRI brain state and accurately forecasts about 5.04 seconds of future activity from 21.6 seconds of resting-state data, while longer synthetic sequences preserve functional connectivity.

  10. QFCQT: A Chaotically Gated Quantformer Framework for Volatile Time-Series Forecasting

    cs.AI 2026-08 reject novelty 4.0 of 10

    QFCQT claims up to 43.9% MSE improvement over HAT on ETTh2 via Lee-oscillator gated activation, but internal table inconsistencies and undefined ablation components undermine the claim.

  11. Diffusion Models for Time Series Forecasting: A Survey

    stat.ML 2025-07 conditional novelty 4.0 of 10

    A survey classifies diffusion-based time series forecasting models into a two-axis taxonomy by conditioning source and integration method.

  12. Spatially-Enhanced Temporal Fusion Transformer: Interpretable Multi-Output Prediction for Parametric Dynamical Systems with Time-Varying Inputs

    cs.LG 2025-05 conditional novelty 4.0 of 10

    A multi-output variant of the Temporal Fusion Transformer, using block-wise masked attention, predicts outputs of three parametric dynamical systems with reported errors mostly below five percent.

  13. Engineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future

    cs.CY 2024-12 conditional novelty 4.0 of 10

    A systematic review of carbon credit computing solutions, covering carbon price and corporate emission prediction algorithms, with a proposed research agenda for carbon management cost forecasting.

  14. Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing

    cs.LG 2025-05 conditional novelty 3.0 of 10

    An n-dimensional numerical Transformer with linear embedding, bin-based discretization, and parallel output heads improves human activity recognition accuracy by 10-15% over a tokenized vanilla Transformer.

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