要在神经网络模型中用于预测的数据的缺失值 [英] Missing values for the data to be used in a Neural Network model for prediction

问题描述

我目前有大量数据将用于训练预测神经网络(美国主要机场的千兆字节天气数据).我几乎每天都有数据，但有些机场的数据中缺少值.例如，一个机场在 1995 年之前可能不存在，所以我没有在那之前那个特定位置的数据.此外，有些缺少整年(可能跨越 1990 年到 2011 年，缺少 2003 年).

I currently have a lot of data that will be used to train a prediction neural network (gigabytes of weather data for major airports around the US). I have data for almost every day, but some airports have missing values in their data. For example, an airport might not have existed before 1995, so I have no data before then for that specific location. Also, some are missing whole years (one might span from 1990 to 2011, missing 2003).

如何在不误导神经网络的情况下使用这些缺失值进行训练?我想用 0 或 -1 填充空数据，但我觉得这会导致网络预测某些输出的这些值.

What can I do to train with these missing values without misguiding my neural network? I though about filling the empty data with 0s or -1s, but I feel like this would cause the network to predict these values for some outputs.

推荐答案

我使用很多 NN 进行预测，我可以说你可以简单地在数据中留下那些漏洞".事实上，NN 能够学习观察到的数据中的关系，所以如果你没有一个特定的时期，那没关系......如果你将空数据设置为一个常数值，你会给你的训练算法误导信息.NN 不需要连续"数据，事实上，在训练之前对数据集进行混洗是一种很好的做法，以便对不连续的样本进行反向传播阶段......

I'm using a lot NNs for forecasting and I can say you that you can simply leave that "holes" in your data. In fact, NNs are able to learn relationships inside observed data and so if you don't have a specific period it doesn't matter...if you set empty data as a constant value you will have give to your training algorithm misleading information. NNs don't need "continuous" data, in fact it's a good practise to shuffle the data sets before training in order to do the backpropagation phase on not-contiguous samples...

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