Converting Forecast Package Plots to Plotly

Introduction

The forecast package is a popular tool for making forecasts of time series data. However, when it comes to creating interactive plots with confidence intervals and projections, we often need to convert the output from the forecast package to Plotly. In this article, we will explore how to do just that.

Step 1: Understanding the Forecast Package

Before we dive into converting forecast packages to Plotly, let’s take a quick look at what the forecast package does. The forecast package provides functions for making forecasts of time series data using various models such as ARIMA, ETS, and more.

library(forecast)

Here’s an example of how we can use the fdeaths dataset to make a simple forecast:

# Load required libraries
library(fabletools)
library(forecast)

# Load data
test_data <- fdeaths

# Make a forecast
forecast <- fdeaths %>% 
  forecast()

forecast

In this example, we load the fdeaths dataset and make a simple forecast using the fdeaths object.

Step 2: Understanding Plotly

Next, let’s take a quick look at what Plotly is. Plotly is an interactive visualization tool that allows us to create a wide range of plots, including line plots, bar plots, scatter plots, and more.

# Load required libraries
library(plotly)

# Create a plotly object
plotly_object <- ggplotly(test_plot)

In this example, we load the test_plot object created using the forecast package and convert it to a Plotly object using ggplotly().

Step 3: Creating Interactive Plots with Confidence Intervals

Now that we understand what the forecast package and Plotly are, let’s explore how to create interactive plots with confidence intervals. To do this, we will use the autoplot() function from the forecast package.

# Load required libraries
library(fabletools)
library(forecast)

# Create a test object
test_data <- fdeaths

# Make an autoplot object
test_plot <- test_data %>% 
  forecast() %>% 
  autoplot()

test_plot

In this example, we create a test object using the fdeaths dataset and make an autoplot() object.

To add confidence intervals to our plot, we can use the forecast.seasonal() function from the forecast package.

# Load required libraries
library(fabletools)
library(forecast)

# Create a test object
test_data <- fdeaths

# Make a forecast with seasonality
forecast_seasonal <- test_data %>% 
  forecast(seasonal = TRUE)

# Extract confidence intervals
ci <- forecast_seasonal$conf.intervals

# Add confidence intervals to the plot
test_plot <- autoplot(forecast_seasonal) + 
  labs(title = "Autoplot with Confidence Intervals",
       subtitle = paste0("Seasonality: ", ci))

test_plot

In this example, we create a test object using the fdeaths dataset and make an autoplot() object. We then extract the confidence intervals using forecast.seasonal()$conf.intervals and add them to our plot.

Step 4: Creating Interactive Plots with Projections

To create interactive plots with projections, we can use the forecast.horizon() function from the forecast package.

# Load required libraries
library(fabletools)
library(forecast)

# Create a test object
test_data <- fdeaths

# Make a forecast horizon
forecast_horizon <- test_data %>% 
  forecast(horizon = "12 months")

# Extract projected values
projected_values <- forecast_horizon$proj

# Add projected values to the plot
test_plot <- autoplot(forecast_horizon) + 
  geom_line(data = projected_values)

test_plot

In this example, we create a test object using the fdeaths dataset and make an autoplot() object. We then extract the projected values using forecast.horizon()$proj and add them to our plot.

Step 5: Converting Forecast Package Plots to Plotly

Finally, let’s explore how to convert forecast package plots to Plotly. To do this, we can use the ggplotly() function from the ggplot2 package.

# Load required libraries
library(ggplot2)

# Create a test object
test_data <- fdeaths

# Make an autoplot object
test_plot <- test_data %>% 
  forecast() %>% 
  autoplot()

# Convert to Plotly
plotly_object <- ggplotly(test_plot)

plotly_object

In this example, we create a test object using the fdeaths dataset and make an autoplot() object. We then convert it to a Plotly object using ggplotly().

Conclusion

In this article, we explored how to convert forecast package plots to Plotly. We started by understanding what the forecast package does and then took a quick look at what Plotly is. We then created interactive plots with confidence intervals and projections using the autoplot() function from the forecast package. Finally, we converted these plots to Plotly using the ggplotly() function.

Additional Resources

Last modified on 2024-01-24