# ---------------------------- 数据预处理 ----------------------------
# rm(list=ls()) # 在 Spaces 中不推荐清空环境变量，每个运行都是独立的
# setwd("/users/songyingxiao/desktop/rworkspace") # 在 Spaces 中不推荐设置工作目录，使用相对路径

# 加载分析所需库
library(zoo)        # 时间序列插值
library(forecast)   # 时间序列预测
library(tseries)    # 平稳性检验
library(ggplot2)    # 可视化
library(uroot)      # 季节性单位根检验
library(readxl)     # 读取Excel数据
library(dplyr)      # 数据处理
library(lubridate)  # 日期处理
library(prophet)    # Prophet模型
library(ggpubr)     # 增强的可视化功能
library(patchwork)  # 图形组合
library(scales)     # 图形比例尺
library(parallel)   # 并行计算
library(doParallel) # 并行计算
library(tidyr)      # 用于 pivot_longer

# 为了解决中文乱码问题，可能需要设置字体
# 如果 Dockerfile 中安装了中文字体，这里可以尝试设置
# if (capabilities("cairo")) {
#   # For cairo-based devices (e.g., png, svg)
#   # For specific font files, you might need to use extrafont package.
#   # For simplicity, if fonts-wqy-zenhei is installed, ggplot2 might pick it up.
#   # Alternatively, use sysfonts and showtext for font handling in R.
#   # library(sysfonts)
#   # library(showtext)
#   # font_add("SimHei", regular = "/usr/share/fonts/wqy-zenhei/wqy-zenhei.ttc")
#   # showtext_auto()
# }


# ---------------------------- 1. 数据清洗 ----------------------------
# 1.1 读取数据
df <- read_excel('gmqrkl.xlsx')

# 1.2 处理零值：将Value列中的0转换为缺失值
df$Value[df$Value == 0] <- NA

# 缺失值插值（线性插值）
df$Value <- na.approx(df$Value, rule = 2, method = "linear")

# 1.3 异常值检测与处理（Z-score法）
z_scores <- abs((df$Value - mean(df$Value, na.rm = TRUE)) / sd(df$Value, na.rm = TRUE))
threshold <- 3
outliers <- df[z_scores > threshold, ]
print("检测到的异常值：")
print(outliers)

# ---------------------------- 2. 时间序列转换 ----------------------------
ts_data <- ts(df$Value, start = c(2023, 1), frequency = 365)

# ---------------------------- 3. 平稳性检验与差分 ----------------------------
make_stationary <- function(data, max_diff = 3) {
  diff_order <- 0  # 初始化差分阶数为 0
  current_data <- data  # 当前处理的时间序列数据

  while(diff_order <= max_diff) {  # 循环进行平稳性检验，直到达到最大差分阶数或数据平稳
    # 检查数据长度是否足够进行 ADF 检验
    if (length(current_data) <= 1) {
      message("数据长度不足，无法进行ADF检验或进一步差分。")
      break
    }
    adf_test <- adf.test(current_data, alternative = "stationary")  # 进行ADF检验

    # par(mfrow = c(2,1)) # 在非交互式环境中直接 plot 可能不会显示
    # plot(current_data, main = paste("差分阶数 d =", diff_order), family = "SimHei") # 绘制时间序列图
    # acf(current_data, main = paste("ACF | d =", diff_order), family = "SimHei") # 绘制自相关函数图

    if(adf_test$p.value < 0.05) break  # 如果ADF检验的 p 值小于 0.05，认为数据平稳，退出循环

    current_data <- diff(current_data)  # 对数据进行一次差分
    diff_order <- diff_order + 1  # 差分阶数加 1
  }

  # par(mfrow = c(1,1)) # 恢复默认图形布局
  return(list(stationary_data = current_data, d = diff_order))  # 返回平稳化后的数据和差分阶数
}

# 调用函数
stationary_result <- make_stationary(ts_data)  # 传入时间序列数据 ts_data
ts_stationary <- stationary_result$stationary_data  # 获取平稳化后的数据
d_order <- stationary_result$d  # 获取差分阶数


# ---------------------------- 4. 白噪声检验 ----------------------------
# 确保 ts_stationary 有足够的长度
if (length(ts_stationary) < 2) {
  stop("平稳化后的序列过短，无法进行白噪声检验。")
}

lb_lag <- min(2*d_order, length(ts_stationary)%/%5)
if (lb_lag == 0) { # 避免 lag=0 的情况
  lb_lag <- 1 # 至少设置为1
}

lb_test <- Box.test(ts_stationary,
                    lag = lb_lag,
                    type = "Ljung-Box")

if(lb_test$p.value > 0.05) {
  stop("序列为白噪声，无需进一步分析")
} else {
  message("通过白噪声检验，p-value = ", round(lb_test$p.value,4))
}
#差分为0
if (d_order == 0) {
  lb_test <- Box.test(ts_stationary, lag = min(10, length(ts_stationary) %/% 5), type = "Ljung-Box")
} else {
  lb_test <- Box.test(ts_stationary, lag = min(2 * d_order, length(ts_stationary) %/% 5), type = "Ljung-Box")
}

if (lb_test$p.value > 0.05) {
  stop("序列为白噪声，无需进一步分析")
} else {
  message("通过白噪声检验，p-value = ", round(lb_test$p.value, 4))
}


# ---------------------------- 5. 季节性检验与处理 ----------------------------
# 方法1：季节单位根检验（确定是否需要季节差分）
# 确保 ts_stationary 有足够的长度和周期
if (length(ts_stationary) < 365 * 2) { # 至少需要两个周期的数据
  message("数据长度不足，可能无法进行可靠的季节性检验（年周期）。")
  ts_obj <- ts(ts_stationary, frequency = 7) # 尝试使用周频率
  seasonal_diff_order <- nsdiffs(ts_obj, test = "ch")
} else {
  ts_obj <- ts(ts_stationary, frequency = 365)
  seasonal_diff_order <- nsdiffs(ts_obj, test = "ch")
}


if(seasonal_diff_order > 0) {
  ts_seasonal <- diff(ts_obj, lag = frequency(ts_obj), differences = seasonal_diff_order)
  message("执行", seasonal_diff_order, "阶季节差分")
} else {
  ts_seasonal <- ts_obj
  message("无需季节差分")
}

# 方法2：STL分解处理多重季节性
# 确保数据长度足以进行多重季节性分解
if (length(ts_stationary) < 365 * 2) {
  message("数据长度不足，可能无法进行可靠的STL多季节性分解（年周期）。跳过此步骤。")
  stl_plot <- NULL # 设置为NULL避免后续错误
} else {
  msts_obj <- msts(ts_stationary, seasonal.periods = c(7, 30, 365))
  stl_decomp <- mstl(msts_obj)

  # 可视化分解结果
  stl_plot <- autoplot(stl_decomp) +
    ggtitle("STL多季节性分解（周+月+年）") +
    theme_bw() +
    theme(text = element_text(family = "SimHei"))
  # print(stl_plot) # 在 Spaces 中，最后统一打印或保存
}

# 提取季节调整后序列
if (!is.null(stl_plot)) {
  ts_season_adj <- stl_decomp[, "Trend"] + stl_decomp[, "Remainder"]
} else {
  ts_season_adj <- ts_stationary # 如果跳过STL，则使用原始平稳化数据
}


# 方法3：Hegyi检验（显式检验周/年季节性）
# 周季节性检验（周期7天）
if (length(ts_data) < 7 * 2) {
  message("数据长度不足，无法进行周季节性Hegyi检验。")
  hegy_weekly <- NULL
} else {
  ts_weekly <- ts(ts_data, frequency = 7)
  hegy_weekly <- hegy.test(ts_weekly, deterministic = c(1,0,0))  # 含常数项，无趋势项
  summary(hegy_weekly)  # 输出检验结果（p<0.05表示存在季节性）
}

# 月季节性检验（周期30天）
if (length(ts_data) < 30 * 2) {
  message("数据长度不足，无法进行月季节性Hegyi检验。")
  hegy_month <- NULL
} else {
  ts_month <- ts(ts_data, frequency = 30)
  hegy_month <- hegy.test(ts_month, deterministic = c(1, 0, 0))
  summary(hegy_month)
}

# 年季节性检验（周期365天）
if (length(ts_data) < 365 * 2) {
  message("数据长度不足，无法进行年季节性Hegyi检验。")
  hegy_annual <- NULL
} else {
  ts_annual <- ts(ts_data, frequency = 365)
  hegy_annual <- hegy.test(ts_annual, deterministic = c(1, 0, 0))
  summary(hegy_annual)
}


# ---------------------------- 6. 窗口大小优化 ----------------------------
ts_values <- as.numeric(df$Value)

# 评估函数
evaluate_window <- function(window_size) {
  n <- length(ts_values)
  if (window_size >= n) return(list(mae = Inf, rmse = Inf))

  # 确保训练数据至少有足够的点进行 auto.arima 训练
  # auto.arima 至少需要一些点才能运行，例如 10-20 点
  if (window_size < 20) { # 设置一个合理的最小窗口大小
    return(list(mae = Inf, rmse = Inf))
  }

  errors <- numeric(n - window_size)
  for (i in 1:(n - window_size)) {
    train <- ts_values[i:(i + window_size - 1)]
    test <- ts_values[i + window_size]

    # 确保训练数据有足够长度进行 ARIMA 建模
    if (length(train) < 2) { # auto.arima 通常需要更多数据
        errors[i] <- NA # 标记为NA或跳过
        next
    }

    model <- tryCatch({
      forecast::auto.arima(
        train,
        d = 0, # 这里固定了 d=0，因为前面已经处理了平稳性
        max.p = 3, max.q = 3,
        stepwise = TRUE
      )
    }, error = function(e) {
      message("auto.arima error: ", e$message, " for window_size ", window_size, " at iteration ", i)
      return(NULL) # 返回NULL表示模型训练失败
    })

    if (is.null(model)) {
      errors[i] <- NA
      next
    }

    fc <- forecast::forecast(model, h = 1)
    errors[i] <- test - fc$mean[1]
  }

  # 移除NA值后计算
  errors <- errors[!is.na(errors)]
  if (length(errors) == 0) {
    return(list(mae = Inf, rmse = Inf))
  }
  return(list(mae = mean(abs(errors)), rmse = sqrt(mean(errors^2))))
}

# 并行计算优化
num_cores <- detectCores(logical = FALSE)
if (num_cores > 1) {
  cl <- makeCluster(num_cores)
  registerDoParallel(cl)
} else {
  message("检测到单核CPU，将不使用并行计算。")
  registerDoSEQ() # 注册顺序执行，以防万一
}


window_sizes <- seq(70, 210, by = 7)
# 过滤掉过大的窗口大小，避免 window_size >= n 导致循环无法进行
window_sizes <- window_sizes[window_sizes < length(ts_values) - 1]

if (length(window_sizes) == 0) {
  stop("可用的窗口大小范围为空，无法进行窗口优化。")
}

results <- foreach(ws = window_sizes, .combine = rbind) %dopar% {
  res <- evaluate_window(ws)
  c(window_size = ws, mae = res$mae, rmse = res$rmse)
}

if (exists("cl") && class(cl) == "cluster") { # 检查集群是否已创建再停止
  stopCluster(cl)
}

# 可视化窗口大小与误差关系
results_df <- as.data.frame(results)
if (nrow(results_df) == 0) {
    message("没有有效的窗口优化结果，跳过窗口优化图表。")
    window_plot <- NULL
    best_mae_window <- 100 # 设置一个默认值
    best_rmse_window <- 100 # 设置一个默认值
} else {
  window_plot <- ggplot(results_df, aes(x = window_size)) +
    geom_line(aes(y = mae, color = "MAE"), size = 1) +
    geom_line(aes(y = rmse, color = "RMSE"), size = 1) +
    geom_point(aes(y = mae, color = "MAE"), size = 2) +
    geom_point(aes(y = rmse, color = "RMSE"), size = 2) +
    labs(title = "窗口大小对预测误差的影响",
         x = "训练窗口天数", y = "误差值",
         color = "指标") +
    theme_minimal() +
    theme(text = element_text(family = "SimHei", size = 12),
          legend.position = "top")

  # print(window_plot)

  # 输出最优窗口
  best_mae_window <- window_sizes[which.min(results_df$mae)]
  best_rmse_window <- window_sizes[which.min(results_df$rmse)]
  cat("最优窗口(MAE):", best_mae_window, "天\n")
  cat("最优窗口(RMSE):", best_rmse_window, "天\n")
}


# ---------------------------- 7. 动态窗口划分 ----------------------------
dynamic_split <- function(data, current_date, window_size = best_mae_window) {
  data %>%
    mutate(Date = lubridate::ymd(Date)) %>%
    filter(Date >= current_date - days(window_size - 1),
           Date <= current_date) %>%
    arrange(Date) %>%
    list(
      train = .,
      test_1w = filter(data, Date > current_date, Date <= current_date + weeks(1)),
      test_4w = filter(data, Date > current_date, Date <= current_date + weeks(4))
    )
}

df$Date <- lubridate::ymd(df$Date) # 确保 df$Date 是日期类型

start_date <- min(df$Date) + days(best_mae_window)
end_date <- max(df$Date) - weeks(4)

# 确保 start_date 不在 end_date 之后
if (start_date > end_date) {
    stop("数据不足以进行动态窗口划分，请检查数据长度和窗口大小。")
}
current_dates <- seq(start_date, end_date, by = "week")

if (length(current_dates) == 0) {
    stop("无法生成 current_dates 序列，请检查日期范围和数据长度。")
}

############################################################################
# ---------------------------- 8. 模型定义 ----------------------------
# 8.1 SARIMA模型函数 - 修复：添加h参数控制预测长度
sarima_model <- function(train_data, h = 28) {
  # 确保 train_data$Value 有足够的长度
  if (length(train_data$Value) < 2) {
    message("SARIMA 训练数据不足，返回NA预测。")
    return(list(mean = rep(NA, h)))
  }
  ts_data <- ts(train_data$Value, frequency = 7) # 假设周季节性
  model <- tryCatch({
    auto.arima(ts_data, seasonal = TRUE)
  }, error = function(e) {
    message("SARIMA 模型训练失败: ", e$message)
    return(NULL)
  })

  if (is.null(model)) {
    return(list(mean = rep(NA, h)))
  }
  fc <- forecast(model, h = h)
  # 确保返回的预测值长度正确
  if (length(fc$mean) < h) {
    fc$mean <- c(fc$mean, rep(NA, h - length(fc$mean)))
  }
  return(fc)
}

# 8.2 Prophet模型函数（修改后）
prophet_model <- function(train_data, test_dates) {
  df_prophet <- train_data %>% rename(ds = Date, y = Value)

  # 确保 Prophet 训练数据有足够的行数
  if (nrow(df_prophet) < 2) {
    message("Prophet 训练数据不足，返回NA预测。")
    return(tibble(Date = test_dates, Value = rep(NA, length(test_dates))))
  }

  # 使用全量数据作为训练集，预测最后四周的测试集
  model <- tryCatch({
    prophet(df_prophet,
            yearly.seasonality = TRUE,
            weekly.seasonality = TRUE)
  }, error = function(e) {
    message("Prophet 模型训练失败: ", e$message)
    return(NULL)
  })

  if (is.null(model)) {
    return(tibble(Date = test_dates, Value = rep(NA, length(test_dates))))
  }

  future <- make_future_dataframe(model, periods = length(test_dates), freq = "day")
  fc <- predict(model, future)

  tibble(
    Date = test_dates,
    Value = tail(fc$yhat, length(test_dates))  # 确保长度一致
  )
}

# 8.3 加权平均组合模型（修改后）
weighted_average_model <- function(train_data, test_dates) {
  # 验证集：从全量数据中提取最后四周
  # 确保 train_data 有足够的历史数据来创建验证集
  validation_start_date <- max(train_data$Date) - weeks(4) + days(1) # 从倒数四周的开始日期
  validation_data <- train_data %>%
    arrange(Date) %>%
    filter(Date >= validation_start_date)

  if (nrow(validation_data) < 28) {
    message("验证集不足四周（少于28天），无法计算权重。将使用默认权重或跳过。")
    sarima_weight <- 0.5
    prophet_weight <- 0.5
  } else {
    # 使用全量数据训练SARIMA和Prophet
    sarima_fc_val <- sarima_model(train_data, h = nrow(validation_data))
    sarima_values <- as.numeric(sarima_fc_val$mean)

    prophet_fc_val <- prophet_model(train_data, validation_data$Date)

    # 确保预测值长度与实际值一致
    min_len <- min(length(validation_data$Value), length(sarima_values), length(prophet_fc_val$Value))
    if (min_len < 1) {
      sarima_weight <- 0.5
      prophet_weight <- 0.5
      message("验证集或预测值长度不足，使用默认权重。")
    } else {
      sarima_mae <- mean(abs(validation_data$Value[1:min_len] - sarima_values[1:min_len]), na.rm = TRUE)
      prophet_mae <- mean(abs(validation_data$Value[1:min_len] - prophet_fc_val$Value[1:min_len]), na.rm = TRUE)

      # 避免除以零或NaN
      if (is.na(sarima_mae) || is.na(prophet_mae) || (sarima_mae == 0 && prophet_mae == 0)) {
        sarima_weight <- 0.5
        prophet_weight <- 0.5
      } else {
        total_mae <- sarima_mae + prophet_mae
        sarima_weight <- 1 - (sarima_mae / total_mae)
        prophet_weight <- 1 - (prophet_mae / total_mae)

        total_weight <- sarima_weight + prophet_weight
        sarima_weight <- sarima_weight / total_weight
        prophet_weight <- prophet_weight / total_weight
      }
    }
  }
  cat("权重计算 - SARIMA:", round(sarima_weight, 2),
      "Prophet:", round(prophet_weight, 2), "\n")

  # 预测最后四周
  full_sarima_fc <- sarima_model(train_data, h = length(test_dates))
  full_sarima <- as.numeric(full_sarima_fc$mean)
  full_prophet <- prophet_model(train_data, test_dates)$Value

  # 确保预测值长度一致
  min_len_pred <- min(length(full_sarima), length(full_prophet), length(test_dates))
  if (min_len_pred < 1) {
    message("最终预测长度不足，返回NA。")
    result <- tibble(Date = test_dates, Value = rep(NA, length(test_dates)))
  } else {
    weighted_avg <- sarima_weight * full_sarima[1:min_len_pred] + prophet_weight * full_prophet[1:min_len_pred]
    result <- tibble(Date = test_dates[1:min_len_pred], Value = weighted_avg)
  }

  attr(result, "sarima_weight") <- sarima_weight
  attr(result, "prophet_weight") <- prophet_weight
  return(result)
}

# ---------------------------- 9. 模型性能评估 ----------------------------
calculate_metrics <- function(actual, predicted) {
  # 移除NA值，并确保长度一致
  common_indices <- intersect(which(!is.na(actual)), which(!is.na(predicted)))
  if (length(common_indices) == 0) {
    return(data.frame(MAE = NA, RMSE = NA, MAPE = NA, sMAPE = NA))
  }
  actual <- actual[common_indices]
  predicted <- predicted[common_indices]

  mae <- mean(abs(actual - predicted))
  rmse <- sqrt(mean((actual - predicted)^2))
  # 避免除以零或NaN
  mape <- ifelse(any(actual == 0), NA, mean(abs((actual - predicted) / actual)) * 100)
  smape <- 200 * mean(abs(actual - predicted) / (abs(actual) + abs(predicted)))

  data.frame(MAE = mae, RMSE = rmse, MAPE = mape, sMAPE = smape)
}

all_metrics <- list()
weight_history <- tibble()  # 存储权重历史

# 限制循环次数以加快测试或处理大数据量
# 例如，只处理最后几个 `current_dates`
# current_dates_to_process <- tail(current_dates, 5) # 只处理最后5个周期
current_dates_to_process <- current_dates

for(i in seq_along(current_dates_to_process)) {
  date <- current_dates_to_process[i]
  message("Processing date: ", date)

  # 动态窗口划分（用于 SARIMA 训练，Prophet 使用 full_train_data）
  window_data <- dynamic_split(df, date, window_size = best_mae_window)

  # 使用全量数据作为训练集
  full_train_data <- df %>% filter(Date <= date)

  # 保留最后四周作为测试集
  test_start_date <- date + days(1)
  test_end_date <- date + weeks(4)
  test_dates <- seq(test_start_date, test_end_date, by = "day")

  if (length(test_dates) == 0) {
    message("测试日期序列为空，跳过此迭代。")
    next
  }
  # 检查实际值数据是否存在且足够
  actual_values_full_range <- df %>%
    filter(Date >= test_start_date, Date <= test_end_date) %>%
    pull(Value)

  if (length(actual_values_full_range) == 0) {
      message("当前日期 ", date, " 之后的实际值不足，跳过此迭代。")
      next
  }

  # 各模型预测
  sarima_fc_result <- sarima_model(window_data$train, h = length(test_dates))
  sarima_pred <- as.numeric(sarima_fc_result$mean)

  prophet_pred_df <- prophet_model(full_train_data, test_dates)
  prophet_pred <- prophet_pred_df$Value

  weighted_pred_df <- weighted_average_model(full_train_data, test_dates)
  weighted_pred <- weighted_pred_df$Value


  # 提取权重
  sarima_weight <- attr(weighted_pred_df, "sarima_weight")
  prophet_weight <- attr(weighted_pred_df, "prophet_weight")

  # 存储权重信息
  weight_history <- bind_rows(weight_history,
                              tibble(Date = date,
                                     SARIMA_Weight = sarima_weight,
                                     Prophet_Weight = prophet_weight))

  # 计算评估指标
  # 确保预测值和实际值长度一致
  min_len_metrics <- min(length(actual_values_full_range), length(sarima_pred),
                         length(prophet_pred), length(weighted_pred))
  if (min_len_metrics == 0) {
      message("预测或实际值长度为0，无法计算指标。")
      next
  }

  actual_values <- actual_values_full_range[1:min_len_metrics]
  sarima_pred_cut <- sarima_pred[1:min_len_metrics]
  prophet_pred_cut <- prophet_pred[1:min_len_metrics]
  weighted_pred_cut <- weighted_pred[1:min_len_metrics]


  sarima_metrics <- calculate_metrics(actual_values, sarima_pred_cut)
  prophet_metrics <- calculate_metrics(actual_values, prophet_pred_cut)
  weighted_metrics <- calculate_metrics(actual_values, weighted_pred_cut)

  all_metrics[[i]] <- list(
    date = date,
    sarima = sarima_metrics,
    prophet = prophet_metrics,
    weighted = weighted_metrics
  )
}

# 过滤掉空的列表元素
all_metrics <- all_metrics[!sapply(all_metrics, is.null)]

# ---------------------------- 10. 模型对比可视化 ----------------------------
# 10.1 提取评估结果
if (length(all_metrics) == 0) {
    stop("没有计算出任何模型评估指标，无法生成图表。请检查数据和循环设置。")
}
metrics_df <- bind_rows(lapply(all_metrics, function(x) {
  bind_rows(
    x$sarima %>% mutate(Model = "SARIMA", Date = x$date),
    x$prophet %>% mutate(Model = "Prophet", Date = x$date),
    x$weighted %>% mutate(Model = "加权平均模型", Date = x$date)
  )
}))

# 10.2 模型权重变化可视化
plot_model_weights <- function(weight_history) {
  # 确保日期是日期格式
  weight_history$Date <- as.Date(weight_history$Date)

  # 绘制权重变化图
  ggplot(weight_history, aes(x = Date)) +
    geom_line(aes(y = SARIMA_Weight, color = "SARIMA Weight"), size = 1) +
    geom_line(aes(y = Prophet_Weight, color = "Prophet Weight"), size = 1) +
    labs(title = "模型权重变化",
         x = "日期",
         y = "权重",
         color = "模型") +
    theme_minimal() +
    theme(text = element_text(family = "SimHei", size = 12)) +
    scale_color_manual(values = c("SARIMA Weight" = "#E41A1C", "Prophet Weight" = "#377EB8"))
}

# 调用函数绘制图形
p_weights <- plot_model_weights(weight_history)


# 10.3 各模型误差指标对比（分面图）
metrics_long <- metrics_df %>%
  pivot_longer(cols = c(MAE, RMSE, MAPE, sMAPE),
               names_to = "Metric",
               values_to = "Value")

error_plot <- ggplot(metrics_long, aes(x = Date, y = Value, color = Model)) +
  geom_line(size = 0.8) +
  facet_wrap(~Metric, scales = "free_y", ncol = 2) +
  labs(title = "各模型预测误差指标对比",
       y = "误差值", x = "预测起始日期") +
  theme_bw() +
  theme(text = element_text(family = "SimHei"),
        legend.position = "bottom") +
  scale_color_brewer(palette = "Set1")

# print(error_plot)

# 10.4 平均性能对比雷达图
avg_metrics <- metrics_df %>%
  group_by(Model) %>%
  summarise(
    MAE = mean(MAE, na.rm = TRUE),
    RMSE = mean(RMSE, na.rm = TRUE),
    MAPE = mean(MAPE, na.rm = TRUE),
    sMAPE = mean(sMAPE, na.rm = TRUE)
  ) %>%
  pivot_longer(cols = -Model, names_to = "Metric", values_to = "Value")

radar_plot <- ggplot(avg_metrics, aes(x = Metric, y = Value, group = Model, color = Model)) +
  geom_polygon(aes(fill = Model), alpha = 0.2, size = 1) +
  coord_polar() +
  labs(title = "各模型平均性能对比雷达图") +
  theme_minimal() +
  theme(text = element_text(family = "SimHei"),
        axis.text.x = element_text(size = 10),
        legend.position = "right")

# print(radar_plot)

# 10.5 最终预测对比图（最后一个窗口）
# 确保 current_dates_to_process 不为空
if (length(current_dates_to_process) == 0) {
  warning("没有足够的 current_dates 来生成最终预测对比图。")
  forecast_plot <- NULL
} else {
  last_date <- tail(current_dates_to_process, 1) # 使用处理过的日期序列
  window_data <- dynamic_split(df, last_date)
  test_dates <- seq(last_date + days(1), last_date + weeks(4), by = "day")

  sarima_fc <- sarima_model(window_data$train, h = length(test_dates))$mean
  prophet_fc <- prophet_model(window_data$train, test_dates)
  weighted_fc <- weighted_average_model(window_data$train, test_dates)

  # 过滤掉NA值，确保长度一致
  min_len_final_comp <- min(length(window_data$test_4w$Value),
                            length(sarima_fc),
                            length(prophet_fc$Value),
                            length(weighted_fc$Value))

  if (min_len_final_comp == 0) {
    warning("最终预测对比图数据长度不足，跳过生成。")
    forecast_plot <- NULL
  } else {
    comparison_df <- bind_rows(
      window_data$test_4w[1:min_len_final_comp,] %>% mutate(Type = "实际值"),
      tibble(Date = test_dates[1:min_len_final_comp], Value = sarima_fc[1:min_len_final_comp], Type = "SARIMA预测"),
      prophet_fc[1:min_len_final_comp,] %>% mutate(Type = "Prophet预测"),
      weighted_fc[1:min_len_final_comp,] %>% mutate(Type = "加权平均预测")
    )

    forecast_plot <- ggplot(comparison_df, aes(x = Date, y = Value, color = Type)) +
      geom_line(size = 1.2) +
      geom_point(size = 2) +
      labs(title = "三种模型未来4周预测对比",
           subtitle = paste("预测起始日期:", last_date),
           x = "日期", y = "值") +
      theme_minimal() +
      theme(text = element_text(family = "SimHei", size = 12),
            legend.position = "top") +
      scale_color_manual(values = c("实际值" = "black",
                                     "SARIMA预测" = "red",
                                     "Prophet预测" = "blue",
                                     "加权平均预测" = "green"))

    # print(forecast_plot)
  }
}


# 10.6 误差分布箱线图
error_dist <- metrics_df %>%
  select(Model, MAE, RMSE, MAPE, sMAPE) %>%
  pivot_longer(cols = -Model, names_to = "Metric", values_to = "Value")

box_plot <- ggplot(error_dist, aes(x = Model, y = Value, fill = Model)) +
  geom_boxplot(alpha = 0.7) +
  facet_wrap(~Metric, scales = "free_y") +
  labs(title = "各模型误差分布箱线图") +
  theme_bw() +
  theme(text = element_text(family = "SimHei"),
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_fill_brewer(palette = "Pastel1")
# print(box_plot)


# 10.7 组合所有可视化结果
# 检查每个图表对象是否为NULL，只有非NULL的才会被组合
plots_to_combine <- list()
if (!is.null(p_weights)) plots_to_combine$p_weights <- p_weights
if (!is.null(error_plot)) plots_to_combine$error_plot <- error_plot
if (!is.null(forecast_plot)) plots_to_combine$forecast_plot <- forecast_plot
if (!is.null(radar_plot)) plots_to_combine$radar_plot <- radar_plot
if (!is.null(box_plot)) plots_to_combine$box_plot <- box_plot
if (!is.null(stl_plot)) plots_to_combine$stl_plot <- stl_plot # 添加 STL 分解图

# 使用 patchwork 动态组合图表
if (length(plots_to_combine) > 0) {
  # 根据可用图表的数量和类型，选择合适的布局
  # 这是一个通用的组合，你可以根据实际生成的图表调整布局
  combined_plots <- wrap_plots(plots_to_combine) +
    plot_annotation(title = "时间序列预测模型综合比较",
                    theme = theme(plot.title = element_text(hjust = 0.5, size = 16, family = "SimHei")))
  print(combined_plots)
  # 保存综合图表
  ggsave("forecast_comparison.png", combined_plots, width = 16, height = 20, dpi = 300)
} else {
  message("没有足够的图表可以组合。")
}


# 输出平均性能
cat("\n各模型平均性能对比：\n")
avg_perf <- metrics_df %>%
  group_by(Model) %>%
  summarise(
    MAE = mean(MAE, na.rm = TRUE),
    RMSE = mean(RMSE, na.rm = TRUE),
    MAPE = mean(MAPE, na.rm = TRUE),
    sMAPE = mean(sMAPE, na.rm = TRUE)
  )
print(avg_perf)


#---------------------------第11部分代码---------------------------#
# 11.1 获取最后一个预测窗口数据
# 确保 current_dates_to_process 不为空
if (length(current_dates_to_process) == 0) {
  warning("没有足够的 current_dates 来进行最后的预测可视化。")
} else {
  last_date <- tail(current_dates_to_process, 1)
  window_data <- dynamic_split(df, last_date)
  test_dates <- seq(last_date + days(1), last_date + weeks(4), by = "day")
  actual_data <- window_data$test_4w

  # 11.2 生成各模型预测
  sarima_fc <- sarima_model(window_data$train, h = 28)$mean
  prophet_fc <- prophet_model(window_data$train, test_dates)
  weighted_fc <- weighted_average_model(window_data$train, test_dates)

  # 11.3 创建结果数据框
  # 确保所有预测结果长度一致
  min_len_results <- min(length(actual_data$Value), length(sarima_fc), length(prophet_fc$Value), length(weighted_fc$Value))

  if (min_len_results == 0) {
      warning("最终结果数据框数据长度不足，无法创建。")
      results_df <- NULL
  } else {
    results_df <- bind_rows(
      actual_data[1:min_len_results,] %>% mutate(Model = "实际值"),
      tibble(Date = as.Date(test_dates[1:min_len_results]), Value = sarima_fc[1:min_len_results], Model = "SARIMA预测"),
      prophet_fc[1:min_len_results,] %>% mutate(Model = "Prophet预测"),
      weighted_fc[1:min_len_results,] %>% mutate(Model = "加权平均预测")
    )

    # 确保所有数据框中的 Date 列都是 Date 类型
    results_df$Date <- as.Date(results_df$Date)
    prophet_fc$Date <- as.Date(prophet_fc$Date)
    weighted_fc$Date <- as.Date(weighted_fc$Date)

    #  ————————————————————————————提取一周预测数据————————————————————————————#
    first_week_df <- results_df %>%
      filter(Date <= last_date + weeks(1))
    # 再次确保 first_week_df 中的 Date 列是 Date 类型
    first_week_df$Date <- as.Date(first_week_df$Date)

    # 11.5 可视化：第一周预测对比
    first_week_plot <- ggplot(first_week_df, aes(x = Date, y = Value, color = Model, linetype = Model)) +
      geom_line(size = 1.2) +
      geom_point(data = filter(first_week_df, Model == "实际值"), size = 2) +
      labs(
        title = "第一周预测结果对比",
        subtitle = paste("预测起始日期:", format(last_date, "%Y-%m-%d")),
        x = "日期", y = "值"
      ) +
      theme_minimal() +
      theme(
        text = element_text(family = "SimHei", size = 12),
        legend.position = "top",
        axis.text.x = element_text(angle = 45, hjust = 1)
      ) +
      scale_color_manual(
        values = c(
          "实际值" = "black",
          "SARIMA预测" = "#E41A1C",
          "Prophet预测" = "#377EB8",
          "加权平均预测" = "#4DAF4A"
        )
      ) +
      scale_linetype_manual(
        values = c(
          "实际值" = "solid",
          "SARIMA预测" = "dashed",
          "Prophet预测" = "dotted",
          "加权平均预测" = "longdash"
        )
      ) +
      scale_x_date(
        date_labels = "%m-%d",
        date_breaks = "1 day"
      )

    print(first_week_plot)

    #---------------------------对比图---------------------------#

    # 11.6 计算第一周的误差指标
    # 计算每个模型的 MAE、MAPE 和 RMSE
    # 提取实际值（第一周）
    actual_values_1w <- first_week_df %>%
      filter(Model == "实际值") %>%
      pull(Value)

    # 提取各模型预测值（第一周）
    sarima_predictions_1w <- first_week_df %>%
      filter(Model == "SARIMA预测") %>%
      pull(Value)
    prophet_predictions_1w <- first_week_df %>%
      filter(Model == "Prophet预测") %>%
      pull(Value)
    weighted_predictions_1w <- first_week_df %>%
      filter(Model == "加权平均预测") %>%
      pull(Value)

    # 检查数据长度是否一致
    min_len_error_1w <- min(length(actual_values_1w), length(sarima_predictions_1w),
                            length(prophet_predictions_1w), length(weighted_predictions_1w))

    if (min_len_error_1w == 0) {
      warning("第一周误差计算数据长度不足。")
      error_df_long_1w <- data.frame() # 创建空数据框以避免后续错误
    } else {
      # 截取到最小长度
      actual_values_1w <- actual_values_1w[1:min_len_error_1w]
      sarima_predictions_1w <- sarima_predictions_1w[1:min_len_error_1w]
      prophet_predictions_1w <- prophet_predictions_1w[1:min_len_error_1w]
      weighted_predictions_1w <- weighted_predictions_1w[1:min_len_error_1w]

      # 计算误差指标
      sarima_error_1w <- calculate_error_metrics(actual_values_1w, sarima_predictions_1w)
      prophet_error_1w <- calculate_error_metrics(actual_values_1w, prophet_predictions_1w)
      weighted_error_1w <- calculate_error_metrics(actual_values_1w, weighted_predictions_1w)

      # 创建误差指标数据框
      error_df_1w <- data.frame(
        Model = c("SARIMA预测", "Prophet预测", "加权平均预测"),
        MAE = c(sarima_error_1w$MAE, prophet_error_1w$MAE, weighted_error_1w$MAE),
        MAPE = c(sarima_error_1w$MAPE, prophet_error_1w$MAPE, weighted_error_1w$MAPE),
        RMSE = c(sarima_error_1w$RMSE, prophet_error_1w$RMSE, weighted_error_1w$RMSE)
      )

      # 将误差指标数据框转换为长格式
      error_df_long_1w <- error_df_1w %>%
        pivot_longer(cols = c(MAE, MAPE, RMSE),
                     names_to = "Metric",
                     values_to = "Value")

      # 11.7 可视化：第一周误差结果对比
      # 创建误差指标对比图
      error_plot_1w <- ggplot(error_df_long_1w, aes(x = Model, y = Value, fill = Model)) +
        geom_bar(stat = "identity", position = position_dodge()) +
        geom_text(aes(label = ifelse(Metric == "MAPE", paste0(round(Value, 2), "%"), round(Value, 2))),
                  position = position_dodge(width = 0.9), vjust = -0.5) +
        labs(
          title = "第一周预测误差结果对比",
          x = "模型", y = "误差值"
        ) +
        facet_wrap(~Metric, scales = "free_y") +
        theme_minimal() +
        theme(
          text = element_text(family = "SimHei", size = 12),
          legend.position = "none",
          strip.text = element_text(face = "bold")
        )

      print(error_plot_1w)
    }

    # #  ————————————————————————————提取四周预测数据————————————————————————————#
    # 时间序列对比图
    # 确保 Date 列是 Date 类型
    results_df$Date <- as.Date(results_df$Date)
    # 确保 last_date 是 Date 类型
    last_date <- as.Date(last_date)

    four_week_plot <- ggplot(results_df, aes(x = Date, y = Value, color = Model, linetype = Model)) +
      geom_line(size = 1.2) +
      geom_point(data = filter(results_df, Model == "实际值"), size = 2) +
      labs(
        title = "最后四周预测结果对比",
        subtitle = paste("预测起始日期:", format(last_date, "%Y-%m-%d")),  # 格式化日期
        x = "日期", y = "值"
      ) +
      theme_minimal() +
      theme(
        text = element_text(family = "SimHei", size = 12),
        legend.position = "top",
        axis.text.x = element_text(angle = 45, hjust = 1)
      ) +
      scale_color_manual(
        values = c(
          "实际值" = "black",
          "SARIMA预测" = "#E41A1C",
          "Prophet预测" = "#377EB8",
          "加权平均预测" = "#4DAF4A"
        )
      ) +
      scale_linetype_manual(
        values = c(
          "实际值" = "solid",
          "SARIMA预测" = "dashed",
          "Prophet预测" = "dotted",
          "加权平均预测" = "longdash"
        )
      ) +
      scale_x_date(
        date_labels = "%m-%d",
        date_breaks = "3 days"
      )

    print(four_week_plot)


    # 11.8 提取四周预测数据
    four_weeks_df <- results_df

    # 11.9 计算四周的误差指标
    # 提取实际值（四周）
    actual_values_4w <- four_weeks_df %>%
      filter(Model == "实际值") %>%
      pull(Value)

    # 提取各模型预测值（四周）
    sarima_predictions_4w <- four_weeks_df %>%
      filter(Model == "SARIMA预测") %>%
      pull(Value)
    prophet_predictions_4w <- four_weeks_df %>%
      filter(Model == "Prophet预测") %>%
      pull(Value)
    weighted_predictions_4w <- four_weeks_df %>%
      filter(Model == "加权平均预测") %>%
      pull(Value)

    # 根据实际值数量调整预测值数量
    n_actual <- length(actual_values_4w)
    min_len_error_4w <- min(n_actual, length(sarima_predictions_4w),
                            length(prophet_predictions_4w), length(weighted_predictions_4w))

    if (min_len_error_4w == 0) {
      warning("四周误差计算数据长度不足。")
      error_df_long_4w <- data.frame()
    } else {
      sarima_predictions_4w <- sarima_predictions_4w[1:min_len_error_4w]
      prophet_predictions_4w <- prophet_predictions_4w[1:min_len_error_4w]
      weighted_predictions_4w <- weighted_predictions_4w[1:min_len_error_4w]
      actual_values_4w <- actual_values_4w[1:min_len_error_4w] # 确保 actual 也被截取

      # 计算误差指标
      sarima_error_4w <- calculate_error_metrics(actual_values_4w, sarima_predictions_4w)
      prophet_error_4w <- calculate_error_metrics(actual_values_4w, prophet_predictions_4w)
      weighted_error_4w <- calculate_error_metrics(actual_values_4w, weighted_predictions_4w)

      # 创建误差指标数据框
      error_df_4w <- data.frame(
        Model = c("SARIMA预测", "Prophet预测", "加权平均预测"),
        MAE = c(sarima_error_4w$MAE, prophet_error_4w$MAE, weighted_error_4w$MAE),
        MAPE = c(sarima_error_4w$MAPE, prophet_error_4w$MAPE, weighted_error_4w$MAPE),
        RMSE = c(sarima_error_4w$RMSE, prophet_error_4w$RMSE, weighted_error_4w$RMSE)
      )

      # 将误差指标数据框转换为长格式
      error_df_long_4w <- error_df_4w %>%
        pivot_longer(cols = c(MAE, MAPE, RMSE),
                     names_to = "Metric",
                     values_to = "Value")

      # 11.10 可视化：四周误差结果对比
      # 创建误差指标对比图
      error_plot_4w <- ggplot(error_df_long_4w, aes(x = Model, y = Value, fill = Model)) +
        geom_bar(stat = "identity", position = position_dodge()) +
        geom_text(aes(label = ifelse(Metric == "MAPE", paste0(round(Value, 2), "%"), round(Value, 2))),
                  position = position_dodge(width = 0.9), vjust = -0.5) +
        labs(
          title = "四周预测误差结果对比",
          x = "模型", y = "误差值"
        ) +
        facet_wrap(~Metric, scales = "free_y") +
        theme_minimal() +
        theme(
          text = element_text(family = "SimHei", size = 12),
          legend.position = "none",
          strip.text = element_text(face = "bold")
        )

      print(error_plot_4w)
    }
}