[英]Forecasting in R using Holt's Linear model
I am trying to forecast data with a downward trend.我正在尝试预测呈下降趋势的数据。 I understand that Holt's linear model might be the better way to do it, but am unsure how I can implement it in R.
我知道 Holt 的线性模型可能是更好的方法,但我不确定如何在 R 中实现它。
The data is as follows:数据如下:
day saleRep
1 1 1001.104
2 11 1000.944
3 21 1000.734
4 31 1000.642
5 41 1000.517
6 51 1000.468
7 61 1000.425
8 71 1000.377
9 81 1000.286
10 91 1000.306
11 101 1000.285
12 111 1000.170
I am trying to achieve a few things:我正在努力实现以下目标:
How can I implement it in R?我如何在 R 中实现它?
Use this to reproduce the code:使用它来重现代码:
day <- seq(1,111, by = 10)
saleRep <- c(1001.104, 1000.944, 1000.734, 1000.642, 1000.517, 1000.468, 1000.425, 1000.377, 1000.286, 1000.306, 1000.285, 1000.170)
df <- data.frame(day, saleRep)
Thank you.谢谢你。
I will use the forecast package and go step-by-step.我将使用预测包并逐步进行。 Load the forecast package and generate an example daily time-series data
加载预测包并生成示例每日时间序列数据
require(forecast)
The toy data玩具数据
x <- c(120:1 + rnorm(120, 12, 3))
Convert data to a ts object将数据转换为 ts 对象
x <- ts(x, frequency = 7, #daily data with no yearly seasonalty
start = 1)
autoplot(x)
Split train and test拆分训练和测试
train <- window(x, end = c(17,1))
test <- window(x, start = c(17,2))
fit and forecast an hw model拟合和预测硬件模型
fc <- forecast::hw(x, h = 7) # h = 7 the lenght of test set
Plot the forecast绘制预测
autoplot(fc)
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