plotBeta: Plots the varying beta coefficients of decomposition

In stR: Seasonal Trend Decomposition Using Regression

Plots the varying beta coefficients of decomposition

Description

plotBeta plots the varying beta coefficients of STR decomposition. It plots coefficients only only for independent seasons (one less season than defined).

Usage

plotBeta(
  x,
  xTime = NULL,
  predictorN = 1,
  dim = c(1, 2, 3),
  type = "o",
  pch = 20,
  palette = function(n) rainbow(n, start = 0, end = 0.7)
)

Arguments

x	Result of STR decomposition.
xTime	Times for data to plot.
predictorN	Predictor number in the decomposition to plot the corresponding beta coefficiets.
dim	Dimensions to use to plot the beta coefficients. When 1, the standard charts are used. When 2, graphics:::filled.contour function is used. When 3, rgl:::persp3d is used. The default value is 1.
type	Type of the graph for one dimensional plots.
pch	Symbol code to plot points in 1-dimensional charts. Default value is 20.
palette	Color palette for 2 - and 3 - dimentional plots.

Author(s)

Alexander Dokumentov

See Also

plot.STR

Examples

fit <- AutoSTR(log(grocery))
for (i in 1:2) plotBeta(fit, predictorN = i, dim = 2)

########################################

TrendSeasonalStructure <- list(
  segments = list(c(0, 1)),
  sKnots = list(c(1, 0))
)
DailySeasonalStructure <- list(
  segments = list(c(0, 48)),
  sKnots = c(as.list(1:47), list(c(48, 0)))
)
WeeklySeasonalStructure <- list(
  segments = list(c(0, 336)),
  sKnots = c(as.list(seq(4, 332, 4)), list(c(336, 0)))
)
WDSeasonalStructure <- list(
  segments = list(c(0, 48), c(100, 148)),
  sKnots = c(as.list(c(1:47, 101:147)), list(c(0, 48, 100, 148)))
)

TrendSeasons <- rep(1, nrow(electricity))
DailySeasons <- as.vector(electricity[, "DailySeasonality"])
WeeklySeasons <- as.vector(electricity[, "WeeklySeasonality"])
WDSeasons <- as.vector(electricity[, "WorkingDaySeasonality"])

Data <- as.vector(electricity[, "Consumption"])
Times <- as.vector(electricity[, "Time"])
TempM <- as.vector(electricity[, "Temperature"])
TempM2 <- TempM^2

TrendTimeKnots <- seq(from = head(Times, 1), to = tail(Times, 1), length.out = 116)
SeasonTimeKnots <- seq(from = head(Times, 1), to = tail(Times, 1), length.out = 24)
SeasonTimeKnots2 <- seq(from = head(Times, 1), to = tail(Times, 1), length.out = 12)

TrendData <- rep(1, length(Times))
SeasonData <- rep(1, length(Times))

Trend <- list(
  name = "Trend",
  data = TrendData,
  times = Times,
  seasons = TrendSeasons,
  timeKnots = TrendTimeKnots,
  seasonalStructure = TrendSeasonalStructure,
  lambdas = c(1500, 0, 0)
)
WSeason <- list(
  name = "Weekly seas",
  data = SeasonData,
  times = Times,
  seasons = WeeklySeasons,
  timeKnots = SeasonTimeKnots2,
  seasonalStructure = WeeklySeasonalStructure,
  lambdas = c(0.8, 0.6, 100)
)
WDSeason <- list(
  name = "Dayly seas",
  data = SeasonData,
  times = Times,
  seasons = WDSeasons,
  timeKnots = SeasonTimeKnots,
  seasonalStructure = WDSeasonalStructure,
  lambdas = c(0.003, 0, 240)
)
TrendTempM <- list(
  name = "Trend temp Mel",
  data = TempM,
  times = Times,
  seasons = TrendSeasons,
  timeKnots = TrendTimeKnots,
  seasonalStructure = TrendSeasonalStructure,
  lambdas = c(1e7, 0, 0)
)
TrendTempM2 <- list(
  name = "Trend temp Mel^2",
  data = TempM2,
  times = Times,
  seasons = TrendSeasons,
  timeKnots = TrendTimeKnots,
  seasonalStructure = TrendSeasonalStructure,
  lambdas = c(0.01, 0, 0)
) # Starting parameter is too far from the optimal value
Predictors <- list(Trend, WSeason, WDSeason, TrendTempM, TrendTempM2)

elec.fit <- STR(
  data = Data,
  predictors = Predictors,
  gapCV = 48 * 7
)

plot(elec.fit,
  xTime = as.Date("2000-01-11") + ((Times - 1) / 48 - 10),
  forecastPanels = NULL
)

plotBeta(elec.fit, predictorN = 4)
plotBeta(elec.fit, predictorN = 5) # Beta coefficients are too "wiggly"

stR documentation built on Sept. 11, 2024, 5:39 p.m.