R:分析应收账款是否随着时间的推移而重复出现

# 示例数据
data <- data.frame(
  date = seq(as.Date("2022-01-01"), as.Date("2022-12-31"), by = "month"),
  receivables = c(1000, 1500, 1200, 1300, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300)
)

# 安装并加载必要的库
install.packages("ggplot2")
install.packages("forecast")

library(ggplot2)
library(forecast)

# 绘制应收账款随时间变化的折线图
ggplot(data, aes(x = date, y = receivables)) +
  geom_line() +
  geom_point() +
  labs(title = "应收账款随时间的变化", x = "日期", y = "应收账款金额") +
  theme_minimal()

# 将应收账款数据转换为时间序列对象
ts_data <- ts(data$receivables, start = c(2022, 1), frequency = 12)

# 绘制时间序列图
plot(ts_data, main = "应收账款时间序列", xlab = "时间", ylab = "应收账款金额")

# 进行时间序列分解
decomposed_ts <- decompose(ts_data)
plot(decomposed_ts)

# 进行自相关和偏自相关分析
acf(ts_data, main = "应收账款自相关图")
pacf(ts_data, main = "应收账款偏自相关图")

# 拟合ARIMA模型
fit <- auto.arima(ts_data)

# 打印模型摘要
summary(fit)

# 进行预测
forecasted <- forecast(fit, h = 12)

# 绘制预测结果
plot(forecasted, main = "应收账款预测", xlab = "时间", ylab = "应收账款金额")