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SENTIMENT AND RULES-BASED EQUITY ANALYSIS USING CUSTOMIZED NEURAL NETWORKS IN MULTI-LAYER, MACHINE LEARNING-BASED MODEL

机译:基于多层机床学习模型中的定制神经网络的情感和基于规则的股权分析

摘要

A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated.
机译:提供了一种数据分析平台,基于对文本和数值数据源的处理来预测未来商品和其他资产的州。 该平台包括一个基于多层机器学习的模型,从自然语言处理引擎中提取文本数据的情绪,评估时间序列分析中的数值数据,并为正在分析的商品或资产产生初始预测。 该平台包括神经网络的多种应用,以从进一步分析收集的相关信息的进一步分析来开发增强的预测。 这些包括商品特异性神经网络,旨在不断开发用于处理商品情绪的分类,以及加强学习,符号网络和无监督的元学习的应用,以提高所产生预测的整体性能和准确性。

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