格式化时间序列数据以使用递归神经网络进行短期预测 [英] Format time-series data for short term forecasting using Recurrent Neural networks

问题描述

我想使用递归神经网络(RNN)预测日间功耗.但是，我发现RNN所需的数据格式(样本，时间步长，功能)令人困惑.让我用一个例子解释一下:

I want to forecast day-ahead power consumption using recurrent neural networks (RNN). But, I find the required data format (samples, timesteps, features) for RNN as confusing. Let me explain with an example as:

我在保管箱上有 power_dataset.csv 包含从6月5日到6月18日的耗电量，每分钟10分钟(每天144次观察).现在，使用 rnn 包装，请按照以下步骤操作

I have power_dataset.csv on dropbox, which contains power consumption from 5 June to 18 June at 10 minutely rate (144 observations per day). Now, to check the performance of RNN using rnn R package, I am following these steps

1. 使用6月5日至16日的数据为6月17日使用的火车模型M
2. 使用M预测6月18日的使用情况，并从6月6日至17日更新使用情况

1. train model M for the usage of 17 June by using data from 5-16 June
2. predict usage of 18 June by using M and updated usage from 6-17 June

我对RNN数据格式的理解是:

My understanding of RNN data format is:

样品:样品或观测值的数量.

Samples: No. of samples or observations.

时间步长:图案重复时的步数.以我为例，一天中有144次观察，因此每连续144次观察构成了时间步.换句话说，它定义了季节性周期.

timesteps: No. of steps when the pattern repeats. In my case, 144 observations occur in a day, so every consecutive 144 observations constitute timesteps. In other words, it defines seasonality period.

功能:功能数量，以我为例，即历史天的消耗时间序列

features: No. of features, which is one in my case, i.e., the consumption time-series of historical days

因此，我的脚本是:

library(rnn)
df <- read.csv("power_dataset.csv")
train <- df[1:2016,] # train set from 5-16 June
test <- df[145:dim(df)[1],] # test set from 6-18 June
# prepare data to train a model
trainX <- train[1:1872,]$power # using only power column now
trainY <- train[1873:dim(train)[1],]$power
# data formatting acc. to rnn as [samples, timesteps, features]
tx <-  array(trainX,dim=c(NROW(trainX),144,1))
ty <-  array(trainY,dim=c(NROW(trainY),144,1))
model <- trainr(X=tx,Y=ty,learningrate = 0.04, hidden_dim = 10, numepochs = 100)

错误输出是:

The sample dimension of X is different from the sample dimension of Y.

由于错误的数据格式而产生该错误.如何正确格式化数据?

The error is generated due to wrong data formatting. How can I format data correctly?

推荐答案

几点:

1. 首先，您需要在输入X中具有相同数量的样本，而在训练数据中必须具有输出Y，在上述实现中，您需要1872个样本用于X，而144个样本用于Y.此外，您的训练数组tx包含相同的列，该列重复了144次，没有太大意义.

1. You need to have same # of samples in the input X and output Y in the training data to start with, in the above implementation you are having 1872 samples for X and 144 samples for Y. Moreover, your training array tx contains same column replicated 144 times, which does not make much sense.

我们可以考虑通过以下几种方式训练RNN或LSTM模型: 在下图中，Model1尝试捕获10分钟时间间隔内的重复模式，而Model2尝试捕获(过去)天中的重复模式.

We can think of training a RNN or LSTM model in a few following ways: In the figure below Model1 tries to capture recurring patterns across the 10 minute time intervals where Model2 tries to capture the recurring pattern across the (previous) days.

# Model1
window <- 144
train <- df[1:(13*window),]$power
tx <- t(sapply(1:13, function(x) train[((x-1)*window+1):(x*window)]))
ty <- tx[2:13,]
tx <- tx[-nrow(tx),]
tx <-  array(tx,dim=c(NROW(tx),NCOL(tx),1))
ty <-  array(trainY,dim=c(NROW(ty),NCOL(ty),1))
model <- trainr(X=tx,Y=ty,learningrate = 0.01, hidden_dim = 10, numepochs = 100)
test <- sapply(2:13, function(x) train[((x-1)*window+1):(x*window)])
pred  <- predictr(model,X=array(test,dim=c(NROW(test),NCOL(test),1)))

# Model2
window <- 144
train <- df[1:(13*window),]$power
tx <- sapply(1:12, function(x) train[((x-1)*window+1):(x*window)])
ty <- train[(12*window+1):(13*window)]
tx <-  array(tx,dim=c(NROW(tx),NCOL(tx),1))
ty <-  array(trainY,dim=c(NROW(ty),1,1))
model <- trainr(X=tx,Y=ty,learningrate = 0.01, hidden_dim = 10, numepochs = 100, seq_to_seq_unsync=TRUE)
test <- sapply(2:13, function(x) train[((x-1)*window+1):(x*window)])
pred  <- predictr(model,X=array(test,dim=c(NROW(test),NCOL(test),1)))

1. 与特征大小相比，您的数据太小而无法训练RNN或LSTM.这就是为什么两个训练过的模型都非常差且无法使用的原因.您可以尝试收集更多数据并学习模型，然后将其用于预测.

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