【发布时间】：2018-01-23 10:09:01
【问题描述】：
我正在尝试在 R (https://github.com/llSourcell/How-to-Predict-Stock-Prices-Easily-Demo) 中复制 Siraj 预测股票价格的代码。
这是我的代码：
url <- "https://raw.githubusercontent.com/llSourcell/How-to-Predict-Stock-Prices-Easily-Demo/master/sp500.csv"
sp500 <- read.csv(url, header = FALSE, stringsAsFactors = FALSE)
colnames(sp500) <- "closingPrice"
# choose sequence length
seq_length <- 50
sequence_length <- seq_length + 1
result <- list()
for (i in 1:(nrow(sp500) - seq_length)){
result[[i]] <- sp500[i : (i + seq_length),1]
}
# normalised data
normalised_data <- list()
for (i in 1:length(result)){
normalised_window <- ((result[[i]] / result[[i]][[1]]) - 1)
normalised_data[[i]] <- normalised_window
}
result <- normalised_data
# test <- do.call(rbind, result)
# define train and test datasets
row <- round(0.9 * length(result))
train <- result[1:as.integer(row)]
# train <- sample(train)
x_train <- lapply(train, '[', -length(train[[1]]))
y_train <- lapply(train, '[', length(train[[1]]))
y_train <- unlist(y_train)
test = result[(as.integer(row)+1):length(result)]
x_test <- lapply(test, '[', -length(test[[1]]))
y_test <- lapply(test, '[', length(test[[1]]))
x_train <- array(as.numeric(unlist(x_train)), dim = c(3709, 50, 1))
x_test <- array(as.numeric(unlist(x_test)), dim = c(412, 50, 1))
# x_train <- as.matrix(x_train, dim = c(3709, 51))
# x_test <- as.matrix(x_test, dim = c(412, 51))
class(x_train)
#########################
# Step 2: Build a model #
#########################
library(keras)
model <- keras_model_sequential()
model %>% layer_lstm(units = 50L, return_sequences = TRUE, input_shape = list(NULL, 1)) %>%
layer_dropout(0.2) %>%
layer_lstm(units = 50L, return_sequences = FALSE) %>%
layer_dropout(0.2) %>%
layer_dense(1L) %>%
layer_activation('linear')
summary(model)
model %>% compile(
optimizer = 'rmsprop',
loss = 'mse'
)
###########################
# Step 2: Train the model #
###########################
model %>% fit(x_train, y_train, epochs=1, batch_size=512, validation_split = 0.05)
################################
# Step 2: Plot the predictions #
################################
predict_sequences_multiple <- function(model, data, window_size, prediction_len){
#Predict sequence of 50 steps before shifting prediction run forward by 50 steps
prediction_seqs = list()
for (i in 1:as.integer(nrow(data)/prediction_len)){
curr_frame = array(data[i*prediction_len,,], dim = c(prediction_len,1,1))
predicted = list()
for (j in 1:prediction_len){
predicted[[j]] <- model$predict(curr_frame)[1]
curr_frame <- curr_frame[2:nrow(curr_frame)]
curr_frame <- array(c(curr_frame, predicted[[j]]), dim = c(prediction_len,1,1))
}
prediction_seqs[[i]] <- unlist(as.numeric(predicted))
}
return(prediction_seqs)
}
predictions <- predict_sequences_multiple(model, x_test, 50, 50)
您可以只运行代码来获取预测值（您需要安装 keras）。最后，我想制作一个如下所示的图表： 如您所见，我想在真实数据值上添加预测值。 原始 Siraj 在 python 中的代码如下所示：
def plot_results_multiple(predicted_data, true_data, prediction_len):
fig = plt.figure(facecolor='white')
ax = fig.add_subplot(111)
ax.plot(true_data, label='True Data')
print 'yo'
#Pad the list of predictions to shift it in the graph to it's correct start
for i, data in enumerate(predicted_data):
padding = [None for p in xrange(i * prediction_len)]
plt.plot(padding + data, label='Prediction')
plt.legend()
plt.show()
如何使用 ggplot 复制此图？ 到目前为止我的代码：
library(tidyr)
library(rowr)
library(ggplot2)
plot_data <- data.frame(y_test = unlist(y_test), stringsAsFactors = FALSE)
plot_data <- cbind.fill(plot_data, predictions, fill = NA)
plot_data <- gather(plot_data, key = "key", value = "value")
plot_data <- plot_data %>% dplyr::group_by(key) %>% dplyr::mutate(n = 1:n())
ggplot(plot_data, aes(x = n, y = value, col = key)) + geom_line()
【问题讨论】：
-
如果您的问题只是关于如何生成图表，就像我认为的那样，那么如果您删除所有用于生成预测的代码并只发布所需的数据集，那就太好了制作图表。