書誌事項
- タイトル別名
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- Proposal of Percolative Learning Method for the Baltic Dry Index Forecasting
- バルチック カイウン シスウ ノ ヨソク ニ タイスル シントウ ガクシュウホウ ノ テイアン
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説明
<p>Japan depends on imports for almost all resources, which are supported by maritime trade. Since the shipping industry is a single global market and highly competitive, it is important to anticipate future fluctuations for stable transportation. On the other hand, existing research on time-series forecasting uses only past observations, making it difficult to predict future fluctuations more accurately. In this paper, we propose a training method of percolative learning model. We apply this method to test problems of predicting future shipping market. The results indicate that the proposed method is more accurate and effective than the conventional method.</p>
収録刊行物
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- 日本船舶海洋工学会論文集
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日本船舶海洋工学会論文集 33 (0), 199-207, 2021
公益社団法人 日本船舶海洋工学会
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詳細情報 詳細情報について
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- CRID
- 1390570699998216320
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- NII論文ID
- 130008080998
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- NII書誌ID
- AA12057769
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- ISSN
- 18811760
- 18803717
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- NDL書誌ID
- 031652167
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
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- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可