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

我目前有很多数据将用于训练一个预测神经网络（美国各主要机场的千兆字节天气数据）。我几乎每天都有数据，但有些机场的数据缺少值。例如，一个机场可能在1995年之前可能不存在，所以我之前在这个具体位置之前没有数据。另外，有些缺少整年（一个可能从1990年到2011年，2003年失踪）。

如果不误导我的神经网络，我可以用这些缺失的值来训练？我虽然用0或-1填充空数据，但我觉得这样会导致网络预测这些值的一些输出。

解决方案

我正在使用很多NN进行预测，我可以说你可以简单地将数据中的hole留下。事实上，NNs能够学习观察数据中的关系，所以如果你没有特定的时期，这并不重要...如果你将空数据设置为一个恒定值，你将会给你的训练算法带来误导性的信息。 NNs不需要连续数据，实际上在训练之前洗牌数据集是一个很好的做法，以便对不连续的样本执行反向传播阶段。

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).

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.

解决方案

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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