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https://doi.org/10.62836/jitp.2023.486
Zheyuan Zhang, Yanhong Liu, & Mingxuan Chen. (2023). Advancing Ad Performance Prediction via Temporal Pattern Disentanglement. Journal of Information, Technology and Policy, 1–17. https://doi.org/10.62836/jitp.2023.486
Volume 1, Issue 1, 2023
Copyright (c) 2023 Zheyuan Zhang, Yanhong Liu, Mingxuan Chen
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Advancing Ad Performance Prediction via Temporal Pattern Disentanglement

The accelerating evolution of online advertising necessitates improved prediction accuracy and optimized placement strategies as critical research priorities. Conventional predictive approaches struggle to process complex, heterogeneous advertising data—particularly in capturing temporal shifts and periodic fluctuations. Addressing these limitations, this paper proposes an integrated methodology employing iTransformer with a Periodicity Decoupling Framework (PDF) for enhanced advertising performance forecasting. iTransformer preserves Transformer’s architecture while innovatively redefining attention mechanisms and feedforward networks to model distinct variables as independent tokens. This paradigm enables superior capture of cross-variable dependencies and multi-scale temporal relationships, significantly enhancing adaptability to intricate datasets. Concurrently, PDF examines periodicity patterns through spectral analysis, precisely isolating regular fluctuations to fortify long-sequence forecasting robustness. Further leveraging self-supervised learning minimizes labeled data dependency, maintaining high generalizability under data scarcity. Empirical validation demonstrates substantial performance gains over state-of-the-art methods, particularly in managing periodic complexities within real-world advertising datasets.

iTransformer periodic decoupling framework online advertising service prediction self-supervised learning data analysis time series prediction

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Supporting Agencies

  1. Funding: Not applicable.