我想知道是否存在训练卷积神经网络进行时间序列分类的代码。
我已经看过一些最近的论文(http://www.fer.unizg.hr/_download/repository/KDI-Djalto.pdf),但我不确定是否存在某些内容,或者我是否自己编写代码。
答案 0 :(得分:1)
完全有可能使用CNN进行时间序列预测,无论是回归还是分类。 CNN擅长寻找本地模式,实际上CNN的工作假设是本地模式无处不在。卷积也是时间序列和信号处理中众所周知的操作。与RNN相比,另一个优势是它们的计算速度非常快,因为它们可以并行化而不是RNN顺序性。
在下面的代码中,我将演示一个案例研究,其中可以使用keras预测R中的电力需求。请注意,这不是分类问题(我没有示例方便)但是修改代码来处理分类问题并不困难(使用softmax输出而不是线性输出和交叉熵损失)。
数据集在fpp2库中可用:
library(fpp2)
library(keras)
data("elecdemand")
elec <- as.data.frame(elecdemand)
dm <- as.matrix(elec[, c("WorkDay", "Temperature", "Demand")])
接下来我们创建一个数据生成器。这用于创建在培训过程中使用的批量培训和验证数据。请注意,此代码是一本更简单的数据生成器版本,可以在书中找到#34; Deep Learning with R&#34; (以及它的视频版本&#34;使用R in Motion进行深度学习&#34;)来自配员出版物。
data_gen <- function(dm, batch_size, ycol, lookback, lookahead) {
num_rows <- nrow(dm) - lookback - lookahead
num_batches <- ceiling(num_rows/batch_size)
last_batch_size <- if (num_rows %% batch_size == 0) batch_size else num_rows %% batch_size
i <- 1
start_idx <- 1
return(function(){
running_batch_size <<- if (i == num_batches) last_batch_size else batch_size
end_idx <- start_idx + running_batch_size - 1
start_indices <- start_idx:end_idx
X_batch <- array(0, dim = c(running_batch_size,
lookback,
ncol(dm)))
y_batch <- array(0, dim = c(running_batch_size,
length(ycol)))
for (j in 1:running_batch_size){
row_indices <- start_indices[j]:(start_indices[j]+lookback-1)
X_batch[j,,] <- dm[row_indices,]
y_batch[j,] <- dm[start_indices[j]+lookback-1+lookahead, ycol]
}
i <<- i+1
start_idx <<- end_idx+1
if (i > num_batches){
i <<- 1
start_idx <<- 1
}
list(X_batch, y_batch)
})
}
接下来,我们指定一些要传递给数据生成器的参数(我们创建两个用于训练的生成器和一个用于验证的生成器)。
lookback <- 72
lookahead <- 1
batch_size <- 168
ycol <- 3
回顾参数是我们想要查看的过去多远以及未来我们想要预测的未来。
接下来,我们分割数据集并创建两个生成器:
train_dm&lt; - dm [1:15000,]
val_dm <- dm[15001:16000,]
test_dm <- dm[16001:nrow(dm),]
train_gen <- data_gen(
train_dm,
batch_size = batch_size,
ycol = ycol,
lookback = lookback,
lookahead = lookahead
)
val_gen <- data_gen(
val_dm,
batch_size = batch_size,
ycol = ycol,
lookback = lookback,
lookahead = lookahead
)
接下来,我们创建一个带卷积层的神经网络并训练模型:
model <- keras_model_sequential() %>%
layer_conv_1d(filters=64, kernel_size=4, activation="relu", input_shape=c(lookback, dim(dm)[[-1]])) %>%
layer_max_pooling_1d(pool_size=4) %>%
layer_flatten() %>%
layer_dense(units=lookback * dim(dm)[[-1]], activation="relu") %>%
layer_dropout(rate=0.2) %>%
layer_dense(units=1, activation="linear")
model %>% compile(
optimizer = optimizer_rmsprop(lr=0.001),
loss = "mse",
metric = "mae"
)
val_steps <- 48
history <- model %>% fit_generator(
train_gen,
steps_per_epoch = 50,
epochs = 50,
validation_data = val_gen,
validation_steps = val_steps
)
最后,我们可以使用一个简单的过程创建一些代码来预测24个数据点的序列,在R评论中进行了解释。
####### How to create predictions ####################
#We will create a predict_forecast function that will do the following:
#The function will be given a dataset that will contain weather forecast values and Demand values for the lookback duration. The rest of the MW values will be non-available and
#will be "filled-in" by the deep network (predicted). We will do this with the test_dm dataset.
horizon <- 24
#Store all target values in a vector
goal_predictions <- test_dm[1:(lookback+horizon),ycol]
#get a copy of the dm_test
test_set <- test_dm[1:(lookback+horizon),]
#Set all the Demand values, except the lookback values, in the test set to be equal to NA.
test_set[(lookback+1):nrow(test_set), ycol] <- NA
predict_forecast <- function(model, test_data, ycol, lookback, horizon) {
i <-1
for (i in 1:horizon){
start_idx <- i
end_idx <- start_idx + lookback - 1
predict_idx <- end_idx + 1
input_batch <- test_data[start_idx:end_idx,]
input_batch <- input_batch %>% array_reshape(dim = c(1, dim(input_batch)))
prediction <- model %>% predict_on_batch(input_batch)
test_data[predict_idx, ycol] <- prediction
}
test_data[(lookback+1):(lookback+horizon), ycol]
}
preds <- predict_forecast(model, test_set, ycol, lookback, horizon)
targets <- goal_predictions[(lookback+1):(lookback+horizon)]
pred_df <- data.frame(x = 1:horizon, y = targets, y_hat = preds)
瞧:
还不错。