Module #12 Assignment

First, I assigned the credit card data to a data frame.:

credit_card_data <- data.frame(

  Month = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"),

  `2012` = c(31.9, 27, 31.3, 31, 39.4, 40.7, 42.3, 49.5, 45, 50, 50.9, 58.5),

  `2013` = c(39.4, 36.2, 40.5, 44.6, 46.8, 44.7, 52.2, 54, 48.8, 55.8, 58.7, 63.4)

  

Next I called on ggplot 2 and created the time series plot:

library(ggplot2)

credit_card_ts <- ts(t(credit_card_data[, -1]), start = c(2012, 1), frequency = 12)

matplot(credit_card_ts, type = "l", lty = 1, col = 1:ncol(credit_card_ts),

        xlab = "Time", ylab = "Charges", main = "Monthly Credit Card Charges")

Next, I employed the exponential soothing model:

credit_card_model <- HoltWinters(credit_card_ts, beta = FALSE, gamma = FALSE)

print(credit_card_model)

Holt-Winters exponential smoothing without trend and without seasonal component. Call: HoltWinters(x = credit_card_ts, beta = FALSE, gamma = FALSE) Smoothing parameters: alpha: 0.4752793 beta : FALSE gamma: FALSE Coefficients: [,1] a 59.86572

> The exponential soothing model gives further insight into forecasting and smoothing the wide parameters of the time series plot. Now the data can be analyzed to make future predictions. A higher smoothing parameter (aplhpa) can indicate more weight is being given to recent observations. Beta and gamma are false, indicating the model is not considering a trend or seasonality component.