Optimizing Online Advertisement Services Predictions: A Data Analysis Approach with iTransformer and Periodicity Decoupling

Authors

  • Diwei Zhu

    1. Hive AI, San Francisco, CA 94105, USA;
    2. New York University, New York, NY 10003, USA
  • Yunxiang Gan

    Moloco, Redwood City, CA 94063, USA
  • Xiaoyang Chen

    Radiawave Co., Ltd., Shen Zhen 518100, China

DOI:

https://doi.org/10.30564/aia.v5i1.7488
Received: 2 September 2023 | Revised: 8 October 2023 | Accepted: 15 October 2023 | Published Online: 14 December 2023

Abstract

With the rapid development of the online advertising industry, improving the accuracy of advertising service predictions and optimizing ad placement strategies has become a critical research topic. Traditional forecasting methods often face challenges when dealing with complex and diverse advertising data, especially in handling temporal features and periodic fluctuations. To address this, this paper proposes a data analysis method based on iTransformer and a Periodic Decoupling Framework (PDF) to optimize online advertising service predictions. Without altering the Transformer network architecture, iTransformer innovatively transforms the functionality of the attention mechanism and feedforward network, treating different variables as independent tokens. This allows the model to effectively capture correlations between variables and temporal features, enhancing its ability to adapt to complex data. Meanwhile, the Periodic Decoupling Framework deeply explores periodic features in sales data, accurately separating regular variations, providing stronger support for long-term sequence forecasting. Finally, the introduction of self-supervised learning reduces reliance on labeled data, enabling the model to maintain strong generalization and performance even in data-scarce scenarios. Experimental results show that this method demonstrates superior performance in advertising service predictions, particularly in handling complex advertising data with periodic fluctuations.

Keywords:

iTransformer; Periodic decoupling framework; Online advertising service prediction; Self-supervised Learning; Data analysis; Time series prediction

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How to Cite

Zhu, D., Gan, Y., & Chen, X. (2023). Optimizing Online Advertisement Services Predictions: A Data Analysis Approach with iTransformer and Periodicity Decoupling. Artificial Intelligence Advances, 5(1), 49–62. https://doi.org/10.30564/aia.v5i1.7488

Issue

Vol. 5 , Iss. 1 (April 2023):

Article Type

Article

License

Copyright © 2023 Diwei Zhu, Yunxiang Gan, Xiaoyang Chen

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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