在多元时间预测LSTM模型中预测未来值 [英] Predicting future values in a multivariate time forecasting LSTM model

问题描述

我对如何使用时间序列多元LSTM模型预测未来结果感到困惑.

I am confused on how to predict future results with a time series multivariate LSTM model.

我正在尝试为股票市场预测建立模型，并且具有以下数据特征

I am trying to build a model for a stock market prediction and I have the following data features

日期每日最高价每日低价体积ClosePrice

Date DailyHighPrice DailyLowPrice Volume ClosePrice

如果我使用直到现在的5年数据来训练模型，并且想要预测明天的ClosePrice，从本质上讲，我将需要预测明天的所有数据功能.这是我感到困惑的地方....因为如果所有数据特征都相互依赖，那么明天的所有数据特征仍然未知时，我该如何预测将来的某一天呢?是否有人有示例代码说明如何处理此问题?

If I train my model on 5 years of data up until today and I want to predict tomorrows ClosePrice, essentially I will need to predict all the data features for tomorrow. This is where I am confused.... Because if all the data features are dependent on one another how do i predict for one day in the future when all the data features for tomorrow are still unknown? Does anyone have any example code on how to deal with this issue?

推荐答案

我决定采用的解决方案是来自keras库的TimeseriesGenerator.

The solution I decided to go with here is a TimeseriesGenerator from the keras library.

https://machinelearningmastery.com/如何在喀拉斯邦使用时间序列生成器进行时间序列预测

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