Data to 7 Oct 2026

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

arXiv 2001.08317 ↗First posted 23 Jan 2020cs.LG stat.MLcited by 129

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Abstract (arXiv)

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.

Key facts. Cited by 129 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2020.

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Citing papers per year: 2021: 30; 2022: 31; 2023: 21; 2024: 20; 2025: 16; 2026: 11.

129 papers.

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2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 246
2021Self-Supervised Representation Learning: Introduction, Advances and Challengescited by 147
2023Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecastingcited by 102
2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 89
2022Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecastingcited by 54
2024A Temporal Kolmogorov-Arnold Transformer for Time Series Forecastingcited by 50
2021Representation learning for neural population activity with Neural Data Transformerscited by 48
2022PanGu-Coder: Program Synthesis with Function-Level Language Modelingcited by 47
2022Transformers in Time-series Analysis: A Tutorialcited by 45
2021Annotating Columns with Pre-trained Language Modelscited by 37
2023BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecastingcited by 26
2022Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecastingcited by 23

38 more

2021Dual Aspect Self-Attention based on Transformer for Remaining Useful Life Predictioncited by 16
2022Transformer-Based Self-Supervised Learning for Emotion Recognitioncited by 16
2022Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemicscited by 16
2022Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalitiescited by 16
2024Emotion and Intent Joint Understanding in Multimodal Conversation: A Benchmarking Datasetcited by 15
2021Automated Essay Scoring Using Transformer Modelscited by 14
2022MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Seriescited by 12
2022W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecastingcited by 12
2021Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregationcited by 11
2025Diffusion Models for Time Series Forecasting: A Surveycited by 11
2021Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noisecited by 10
2022Time-series Transformer Generative Adversarial Networkscited by 10
2022Recognition and Prediction of Surgical Gestures and Trajectories Using Transformer Models in Robot-Assisted Surgerycited by 10
2023A Systematic Review for Transformer-based Long-term Series Forecastingcited by 10
2025Deep Learning for Time Series Forecasting: A Surveycited by 10
2021Deep Autoregressive Models with Spectral Attentioncited by 9
2021MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Datacited by 9
2024From Pixels to Predictions: Spectrogram and Vision Transformer for Better Time Series Forecastingcited by 9
2021NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecastingcited by 8
2021TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecastingcited by 8
2021Empirical Quantitative Analysis of COVID-19 Forecasting Modelscited by 8
2022Data-Centric Epidemic Forecasting: A Surveycited by 8
2022Learning Differential Operators for Interpretable Time Series Modelingcited by 7
2023Transformer Training Strategies for Forecasting Multiple Load Time Seriescited by 6
2021Time Series Analysis and Modeling to Forecast: a Surveycited by 5
2022Time Series Forecasting (TSF) Using Various Deep Learning Modelscited by 4
2022Transformer-based conditional generative adversarial network for multivariate time series generationcited by 4
2022Time Series Forecasting with Hypernetworks Generating Parameters in Advancecited by 4
2023Exploring the Advantages of Transformers for High-Frequency Tradingcited by 4
2023Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcarecited by 4
2023One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platformscited by 4
2025Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Predictioncited by 4
2025Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreakcited by 4
2021Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Seriescited by 3
2021Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Pricescited by 3
2021Generative Pre-Trained Transformer for Cardiac Abnormality Detectioncited by 3
2021Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wellscited by 3
2021Flight Demand Forecasting with Transformerscited by 3

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