Time Series Forecasting in R

In R language, you can use various packages for time series analysis to make time series predictions. Some of the most commonly used packages include forecast, fable, and tsibble. Here is a simple example of time series prediction.

# 安装并加载必要的包
install.packages("fable")
library(fable)

# 读取时间序列数据
data <- ts(data, frequency = 12)

# 创建时间序列对象
ts_data <- as_tsibble(data)

# 拟合时间序列模型
model <- ts_data %>%
  model(ARIMA = ARIMA(value))

# 进行预测
forecast <- model %>%
  forecast(h = 12)

# 输出预测结果
print(forecast)

In the example above, the fable package is first installed and loaded, followed by reading time series data and converting it to a tsibble object. Next, the data is fitted using an ARIMA model and forecasted. Finally, the forecast results are output. Adjusting model parameters and forecasting methods based on actual circumstances can lead to more accurate predictions.