In R, you can perform time series analysis, including forecasting and ARIMA models. Here are some examples:
## Forecasting
# Load the forecast package
library(forecast)
# Generate some data
set.seed(123)
x <- ts(rnorm(100), start = c(2020, 1), frequency = 12)
# Fit a forecast model
model <- auto.arima(x)
summary(model) # View the model summary
# Make predictions for future data
new_x <- forecast(model, h = 12)
# Plot the data and the forecasts
plot(x)
lines(new_x$mean, col = "red")
In this code, we first load the `forecast` package. We then generate some data using the `ts()` function, specifying the start year and frequency. We fit a forecast model using the `auto.arima()` function, which automatically selects the best ARIMA model based on the data. We can view a summary of the model using the `summary()` function. We then make predictions for future data using the `forecast()` function, specifying the number of time periods to predict with the `h` argument. Finally, we plot the data and the forecasts using the `plot()` and `lines()` functions.
## ARIMA models
# Load the TSA package
library(TSA)
# Generate some data
set.seed(123)
x <- ts(rnorm(100), start = c(2020, 1), frequency =12)
# Fit an ARIMA model
model <- arima(x, order = c(1, 1, 1))
summary(model) # View the model summary
# Make predictions for future data
new_x <- predict(model, n.ahead = 12)
# Plot the data and the forecasts
plot(x)
lines(new_x$pred, col = "red")
In this code, we first load the `TSA` package. We then generate some data using the `ts()` function, specifying the start year and frequency. We fit an ARIMA model using the `arima()` function, specifying the order of the model as (1, 1, 1). We can view a summary of the model using the `summary()` function. We then make predictions for future data using the `predict()` function, specifying the number of time periods to predict with the `n.ahead` argument. Finally, we plot the data and the forecasts using the `plot()` and `lines()` functions.
Note that there are many other types of time series models available in R, including seasonal ARIMA models and exponential smoothing models. You can find more information on these functions in the R documentation or online tutorials and resources.