The forecast package gives the functions which are used to automatic selection of exponential and ARIMA models.
In R, the forecast package is a powerful and widely used package for time series forecasting. It provides various functions and tools for analyzing and forecasting time series data. Here’s an explanation of the key features and functionalities of the forecast package:
Time Series Decomposition:
The stl() function in the forecast package is often used for decomposing time series data into its components: trend, seasonal, and remainder (residuals). This decomposition can help in understanding the underlying patterns in the data.
library(forecast)
ts_data <- ts(c(5, 8, 12, 18, 24, 30, 35, 40, 45), frequency = 1)
decomposition <- stl(ts_data, s.window = “periodic”)
plot(decomposition)
Time Series Forecasting Models:
The auto.arima() function is a key feature that automatically selects the best ARIMA model for your time series data. ARIMA (AutoRegressive Integrated Moving Average) models are widely used for time series forecasting.
library(forecast)
ts_data <- ts(c(5, 8, 12, 18, 24, 30, 35, 40, 45), frequency = 1)
arima_model <- auto.arima(ts_data)
forecast_result <- forecast(arima_model, h = 3)
plot(forecast_result)
Exponential Smoothing Models:
The ets() function supports various exponential smoothing models, including simple exponential smoothing (SES), Holt’s method (double exponential smoothing), and Holt-Winters method (triple exponential smoothing).
library(forecast)
ts_data <- ts(c(5, 8, 12, 18, 24, 30, 35, 40, 45), frequency = 1)
ets_model <- ets(ts_data)
forecast_result <- forecast(ets_model, h = 3)
plot(forecast_result)
Accuracy Measures:
The package provides functions such as accuracy() to evaluate the accuracy of your forecast models by comparing them to the actual values.
library(forecast)
ts_data <- ts(c(5, 8, 12, 18, 24, 30, 35, 40, 45), frequency = 1)
arima_model <- auto.arima(ts_data)
forecast_result <- forecast(arima_model, h = 3)
accuracy(forecast_result, ts_data)
Visualization:
The plot() function is used to visualize the forecast results, including the predicted values, confidence intervals, and observed data.
library(forecast)
ts_data <- ts(c(5, 8, 12, 18, 24, 30, 35, 40, 45), frequency = 1)
arima_model <- auto.arima(ts_data)
forecast_result <- forecast(arima_model, h = 3)
plot(forecast_result)
These examples provide a basic overview of the forecast package’s capabilities in R. Depending on your specific needs, you may explore additional features and options provided by the package.