给定时间序列csv文件data1:https://pan.baidu.com/s/1NE2Hm17Knid4uaBBkEjw8g
提取码:us7d
因变量y为文件中的VIX,独立变量x的第一列和第二列为截点项和VIX滞后(x_0=0),进行提前一步的滚动窗口练习。
需要将窗口长度设置为3000,并对下一个期间y_t+1进行预测;从头开始,一直滚到最后;对于每次滚动,使用lasso和ridge进行预测,λ=1。
请问一下这个问题应该怎么解决?
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R语言时间序列 滚动窗口预测
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5条回答 默认 最新
- weixin_53295371 2023-02-03 10:21关注
下面是一个可能的 R 代码实现:
# Load required libraries library(glmnet) # Load data data <- read.csv("file.csv") # Define parameters window_size <- 3000 lambda <- 1 # Split data into X and y y <- data$VIX X <- data.frame(Intercept = rep(1, length(y)), Lag = c(0, head(y, -1))) # Loop through the data using a rolling window for (i in 1:(length(y) - window_size)) { y_train <- y[i:(i + window_size - 1)] X_train <- X[i:(i + window_size - 1), ] # Fit Lasso and Ridge models fit_lasso <- glmnet(as.matrix(X_train), y_train, alpha = 1, lambda = lambda) fit_ridge <- glmnet(as.matrix(X_train), y_train, alpha = 0, lambda = lambda) # Predict next step y_lasso_pred <- predict(fit_lasso, newx = as.matrix(X[i + window_size, ])) y_ridge_pred <- predict(fit_ridge, newx = as.matrix(X[i + window_size, ])) # Do something with the predictions (e.g. store them, compare them, etc.) }
对于每次滚动,您可以存储 Lasso 和 Ridge 模型的预测结果,然后可以使用这些预测结果进行评估。具体而言,您可以计算预测值与实际值的误差,并计算误差的平均值、方差、中位数等。此外,您还可以使用图形和散点图来可视化预测结果。
例如,下面是存储预测结果和评估预测误差的代码示例:
# Load required libraries library(glmnet) library(ggplot2) # Load data data <- read.csv("file.csv") # Define parameters window_size <- 3000 lambda <- 1 # Split data into X and y y <- data$VIX X <- data.frame(Intercept = rep(1, length(y)), Lag = c(0, head(y, -1))) # Initialize vectors to store predictions y_lasso_preds <- numeric(length(y) - window_size) y_ridge_preds <- numeric(length(y) - window_size) # Loop through the data using a rolling window for (i in 1:(length(y) - window_size)) { y_train <- y[i:(i + window_size - 1)] X_train <- X[i:(i + window_size - 1), ] # Fit Lasso and Ridge models fit_lasso <- glmnet(as.matrix(X_train), y_train, alpha = 1, lambda = lambda) fit_ridge <- glmnet(as.matrix(X_train), y_train, alpha = 0, lambda = lambda) # Predict next step y_lasso_preds[i] <- predict(fit_lasso, newx = as.matrix(X[i + window_size, ])) y_ridge_preds[i] <- predict(fit_ridge, newx = as.matrix(X[i + window_size, ])) } # Calculate prediction errors errors_lasso <- y[(window_size + 1):length(y)] - y_lasso_preds errors_ridge <- y[(window_size + 1):length(y)] - y_ridge_preds # Plot prediction errors ggplot(data.frame(error = errors_lasso, model = "Lasso"), aes(x = error)) + geom_histogram
以及
ggplot(data.frame(error = errors_ridge, model = "Ridge"), aes(x = error)) + geom_histogram + ggtitle("Histogram of Prediction Errors") + xlab("Error") + ylab("Frequency") + facet_wrap(~ model)
您可以使用上面的代码示例作为起点,并根据您的需求进行修改。
本回答被题主选为最佳回答 , 对您是否有帮助呢?解决 2无用
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