Time Series Analysis using Facebook Prophet in R Programming
ts()
Mathematical Equation of Prophet Model
where, y(t) refers to the forecast g(t) refers to the trend s(t) refers to the seasonality h(t) refers to the holidays for the forecast e(t) refers to the error term while forecasting
Some Important Terms Used in Facebook Prophet Model
- Trend
A trend is a shift in development either in increment or decrement direction. Mathematically,
where, C indicates the carry capacity k indicates the growth m indicates the offset parameter
- Seasonality Seasonality is a feature of time series object that occurs at a particular time/season and changes the trend.
- Holidays Holidays are a time period that changes a lot to the business. It can make a profit or loss depending upon the business.
Implementation in R
Step 1:
# Install the library install.packages("prophet") |
Step 2:
# Load the library library(prophet) |
Step 3:
here
# Dataset ap <- read.csv("example_air_passengers.csv") |
Step 4:
m <- prophet(ap) |
Step 5:
# Predictions future <- make_future_dataframe(m, periods = 365) # Print predictions cat("\nPredictions:\n") tail(future) |
Output:
Predictions:
ds
504 1961-11-26
505 1961-11-27
506 1961-11-28
507 1961-11-29
508 1961-11-30
509 1961-12-01
Step 6:
# Forecast forecast <- predict(m, future) tail(forecast[c('ds', 'yhat', 'yhat_lower', 'yhat_upper')]) |
ds yhat yhat_lower yhat_upper 504 1961-11-26 497.2056 468.6301 525.7918 505 1961-11-27 496.0703 467.8579 525.6728 506 1961-11-28 493.1698 465.5788 522.9650 507 1961-11-29 496.0497 469.2889 524.6313 508 1961-11-30 492.8452 463.7279 520.4519 509 1961-12-01 493.6417 466.3496 522.9887
Step 7:
# Output to be present # As PNG file png(file = "facebookprophetGFG.png") # Plot plot(m, forecast) # Saving the file dev.off() |
Output:
Black dots
Dark blue line
Light blue area
Step 8:
# Output to be present # As PNG file png(file = "facebookprophettrendGFG.png") # Plot prophet_plot_components(m, forecast) # Saving the file dev.off() |
Output:
Contact Us