In RamiKrispin/forecastLM: Time Series Forecasting with Linear Regression Models

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
   fig.width=8
)

library(forecastLM)

library(TSstudio)
data(USgas)

ts_info(USgas)

ts_plot(USgas,
        title = "US Monthly Natural Gas Consumption",
        Xtitle = "U.S. Bureau of Transportation Statistics, Natural Gas Consumption [NATURALGAS]",
        Ytitle = "Billion Cubic Feet"
        )

ts_decompose(USgas)

md1 <- trainLM(input = USgas,
               trend = list(linear = TRUE))

plot_fit(md1)

md2 <- trainLM(input = USgas,
               trend = list(linear = FALSE, log = TRUE))

plot_fit(md2)

md3 <- trainLM(input = USgas,
               trend = list(linear = TRUE, log = TRUE))

plot_fit(md3)

md4 <- trainLM(input = USgas,
               trend = list(linear = FALSE, log = FALSE, power = 1:3))

plot_fit(md4)

md5 <- trainLM(input = USgas,
               trend = list(linear = FALSE, log = FALSE, power = NULL),
               splines = list(break1 =  list(type = "linear", knots = as.Date("2010-01-01"))))

plot_fit(md5)


head(md5$series)
tail(md5$series)

md6 <- trainLM(input = USgas,
               seasonal = "month",
               trend = list(linear = TRUE, log = FALSE, power = NULL),
               splines = list(break1 =  list(type = "linear", knots = as.Date("2010-01-01"))))

plot_fit(md6)