Scholarlink

<p>We employ a Fuzzy Inference System, with a specific focus on utilizing a hybrid intelligent system known as ANFIS (Adaptive Neuro Fuzzy Inference System) to forecast the Baltic Dry Index. This system integrates the adaptive learning features of neural networks with the logical reasoning of fuzzy logic, thereby offering superior forecasting accuracy compared to single-method approaches. Our findings demonstrate the superior performance of the ANFIS model in comparison to a feed-forward neural network and two traditional models, namely AR (Autoregressive) and ARMA (Autoregressive Moving Average), in terms of Root Mean Squared Error (RMSE).</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Baltic dry index forecasting using a neuro-fuzzy inference system

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月8日

查看全文
  • Ioanna Atsalaki,
  • George S. Atsalakis,
  • Konstantinos D. Melas,
  • Nektarios A. Michail

摘要

We employ a Fuzzy Inference System, with a specific focus on utilizing a hybrid intelligent system known as ANFIS (Adaptive Neuro Fuzzy Inference System) to forecast the Baltic Dry Index. This system integrates the adaptive learning features of neural networks with the logical reasoning of fuzzy logic, thereby offering superior forecasting accuracy compared to single-method approaches. Our findings demonstrate the superior performance of the ANFIS model in comparison to a feed-forward neural network and two traditional models, namely AR (Autoregressive) and ARMA (Autoregressive Moving Average), in terms of Root Mean Squared Error (RMSE).