Zero-Inflated Poisson Transformer model for Count Time-Series Data
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- KIMURA Daichi
- NTT Communications
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- IZUMITANI Tomonori
- NTT Communications
Bibliographic Information
- Other Title
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- カウント時系列データに対するゼロ過剰ポアソンTransformerモデル
Abstract
<p>Long sequence time-series forecasting for counting quantities such as demand, sales, and transactions in stock market is important for various business areas. These kinds of real-world data have properties: such as time dependency, non-linearity, non-Gaussian distribution, zero-inflated and integer values. In this study, we propose a time-series forecasting model for zero-inflated count data. To consider time dependency and obtain long-term outputs, we utilize the Informer which is a long sequence time-series forecasting method based on the Transformer. In addition, we suppose a Poisson distribution and a Bernoulli distribution for the outputs of Informer models to deal with zero-inflated count data properties. We evaluated the method using two artificial and two real-world datasets. The results show that the proposed method can make precise forecasts with long-term adaptation to various trend lines. In particular, the proposed method showed highest prediction accuracy in five of the six experimental conditions using real datasets.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2023 (0), 1B3GS203-1B3GS203, 2023
The Japanese Society for Artificial Intelligence
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Keywords
Details 詳細情報について
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- CRID
- 1390859758174422016
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- ISSN
- 27587347
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed