商品价格预测作出明智的决策,而交易使用长短期记忆(LSTM)算法

Shashikant Suman, P. Kaushik, Sai Sri Nandan Challapalli, B. P. Lohani, Pradeep Kushwaha, A. Gupta
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引用次数: 0

摘要

商品市场是指市场参与者聚集在一起买卖原油、黄金、铜、银、棉花和小麦等商品的实体或虚拟市场。人们根据一些预测来投资他们的血汗钱,从商品市场中获得一些利润。虽然传统的方法如技术分析和基本面分析在交易者中非常流行,但它们不如长短期记忆(LSTM)算法分析准确。在本文中,我们开发了一个众所周知的高效LSTM算法模型,通过利用历史数据中具有开盘价,高点,低点和收盘价的商品市场的自由访问数据集来预测商品市场价格。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Commodity Price Prediction for making informed Decisions while trading using Long Short-Term Memory (LSTM) Algorithm
Commodity markets are physical or virtual marketplaces where market players meet to buy or sell positions in commodities such as crude oil, gold, copper, silver, cotton, and wheat. People invest their hard-earned money based on some predictions to gain some profit from commodity market. Although, traditional methods such as technical analysis & fundamental analysis are very popular among traders, they are not as accurate as analysis by long short-term memory (LSTM) algorithm. In this paper, we have developed a model of well-known efficient LSTM algorithm to predict the commodity market price by utilizing a freely accessible dataset for commodity markets having open, high, low, and closing prices from historical data.
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