机器学习——如何使用过去的 20 行作为 X 的每个 Y 值的输入 [英] machine learning-how to use the past 20 rows as an input for X for each Y value

问题描述

我这里有一个非常简单的机器学习代码:

I have a very simple machine learning code here:

# load dataset
dataframe = pandas.read_csv("USDJPY,5.csv", header=None)
dataset = dataframe.values
X = dataset[:,0:59]
Y = dataset[:,59]
#fit Dense Keras model
model.fit(X, Y, validation_data=(x,y_test), epochs=150, batch_size=10)

我的 X 值是 59 个特征，第 60 列是我的 Y 值，一个简单的 1 或 0 分类标签.

My X values are 59 features with the 60th column being my Y value, a simple 1 or 0 classification label.

考虑到我使用的是财务数据，我想回顾过去的 20 个 X 值以预测 Y 值.

Considering that I am using financial data, I would like to lookback the past 20 X values in order to predict the Y value.

那么如何让我的算法使用过去 20 行作为 X 的每个 Y 值的输入?

So how could I make my algorithm use the past 20 rows as an input for X for each Y value?

我对机器学习比较陌生，花了很多时间在网上寻找解决我的问题的方法，但我找不到任何简单的东西.

I'm relatively new to machine learning and spent much time looking online for a solution to my problem yet I could not find anything simple as my case.

有什么想法吗?

推荐答案

这通常是通过循环神经网络 (RNN) 完成的，当接收到下一个输入时，它会保留前一个输入的一些记忆.这是对发生的事情的一个非常简短的解释，但互联网上有很多资源可以更好地理解它们的工作原理.

This is typically done with Recurrent Neural Networks (RNN), that retain some memory of the previous input, when the next input is received. Thats a very breif explanation of what goes on, but there are plenty of sources on the internet to better wrap your understanding of how they work.

让我们用一个简单的例子来分解它.假设您有 5 个样本和 5 个数据特征，并且您希望将两个数据错开 2 行而不是 20 行.这是您的数据(假设 1 只股票并且最早的价格值在前).我们可以将每一行视为一周中的一天

Lets break this down in a simple example. Lets say you have 5 samples and 5 features of data, and you want two stagger the data by 2 rows instead of 20. Here is your data (assuming 1 stock and the oldest price value is first). And we can think of each row as a day of the week

ar = np.random.randint(10,100,(5,5))

[[43, 79, 67, 20, 13],    #<---Monday---
 [80, 86, 78, 76, 71],    #<---Tuesday---
 [35, 23, 62, 31, 59],    #<---Wednesday---
 [67, 53, 92, 80, 15],    #<---Thursday---
 [60, 20, 10, 45, 47]]    #<---Firday---

要在 keras 中使用 LSTM，您的数据需要是 3-D 的，而不是现在的当前 2-D 结构，并且每个维度的符号是 (samples,时间步长，特征).目前您只有 (samples,features)，因此您需要扩充数据.

To use an LSTM in keras, your data needs to be 3-D, vs the current 2-D structure it is now, and the notation for each diminsion is (samples,timesteps,features). Currently you only have (samples,features) so you would need to augment the data.

a2 = np.concatenate([ar[x:x+2,:] for x in range(ar.shape[0]-1)])
a2 = a2.reshape(4,2,5)

[[[43, 79, 67, 20, 13],    #See Monday First
  [80, 86, 78, 76, 71]],   #See Tuesday second ---> Predict Value originally set for Tuesday
 [[80, 86, 78, 76, 71],    #See Tuesday First
  [35, 23, 62, 31, 59]],   #See Wednesday Second ---> Predict Value originally set for Wednesday
 [[35, 23, 62, 31, 59],    #See Wednesday Value First
  [67, 53, 92, 80, 15]],   #See Thursday Values Second ---> Predict value originally set for Thursday
 [[67, 53, 92, 80, 15],    #And so on
  [60, 20, 10, 45, 47]]])

注意数据是如何交错和 3 维的.现在只需制作一个 LSTM 网络.Y 仍然是二维的，因为这是一个多对一的结构，但是您需要剪辑第一个值.

Notice how the data is staggered and 3 dimensional. Now just make an LSTM network. Y remains 2-D since this is a many-to-one structure, however you need to clip the first value.

model = Sequential()
model.add(LSTM(hidden_dims,input_shape=(a2.shape[1],a2.shape[2]))
model.add(Dense(1))

这只是让您动起来的一个简短示例.有许多不同的设置可以工作(包括不使用 RNN)，您需要为您的数据找到正确的设置.

This is just a brief example to get you moving. There are many different setups that will work (including not using RNN), you need to find the correct one for your data.

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