library(dplyr)
library(ggplot2)
# Apply Transformations
# - Differencing transformation to identify ARIMA & SARIMA Orders
m4_hourly %>%
group_by(id) %>%
plot_acf_diagnostics(
date, value, # ACF & PACF
.lags = "7 days", # 7-Days of hourly lags
.interactive = FALSE
)
# Apply Transformations
# - Differencing transformation to identify ARIMA & SARIMA Orders
m4_hourly %>%
group_by(id) %>%
plot_acf_diagnostics(
date,
diff_vec(value, lag = 1), # Difference the value column
.lags = 0:(24*7), # 7-Days of hourly lags
.interactive = FALSE
) +
ggtitle("ACF Diagnostics", subtitle = "1st Difference")
# CCFs Too!
walmart_sales_weekly %>%
select(id, Date, Weekly_Sales, Temperature, Fuel_Price) %>%
group_by(id) %>%
plot_acf_diagnostics(
Date, Weekly_Sales, # ACF & PACF
.ccf_vars = c(Temperature, Fuel_Price), # CCFs
.lags = "3 months", # 3 months of weekly lags
.interactive = FALSE
)
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