Detect slopes in time series

Use recursive partitioning function rpart::rpart() to perform a 'classification' of relatively stable slopes in a timeseries.

ts_slopes(
  y,
  minDataSplit = NROW(y)/4,
  minSlopeDuration = round(minDataSplit/3),
  changeSensitivity = 0.01,
  maxSlopes = floor(NROW(y)/minSlopeDuration),
  method = c("anova", "poisson", "class", "exp")[1],
  minChange = NA,
  doSlopePlot = FALSE,
  doTreePlot = FALSE
)

Arguments

y

A time series of numeric vector

minDataSplit

An integer indicating how many datapoints should be in a segment before it will be analysed for presence of a slope (default = 12)

minSlopeDuration

Minimum duration (number of datapoints) of a slope (default = round(minDataSplit/3))

changeSensitivity

A number indicating a criterion of change that must occur before declaring the presence of a slope Higher numbers indicate higher levels of change must occur before a slope is considered. For example, if method = "anova", the overall R^2 after a slope is introduced must increase by the value of changeSensitivity, see the cp parameter in rpart::rpart.control(). (default = 0.01)

maxSlopes

Maximum number of levels in one series (default = floor(max(NROW(y), na.rm = TRUE)/minSlopeDuration))

method

The partitioning method to use, see the manual pages of rpart for details.

minChange

After the call to rpart, adjust detected slope value to a minimum absolute change in y. If a slope value is smaller than minChange, the previous slope will be continued. Set e.g. to sd(diff(y), na.rm = TRUE). Note that this is an iterative process starting at the beginning of the series and 'correcting' towards the end. The results are stored in p_adj. Set to NA to skip, which means p_adj will be identical to p (default = NA)

doSlopePlot

Should a plot with the original series and the levels be produced? (default = FALSE)

doTreePlot

Should a plot of the decision tree be produced. This requires package partykit (default = FALSE)

Value

A list object with fields tree and pred. The latter is a data frame with columns x (time), y (the variable of interest) and p the predicted slopes in y and p_adj, the slopes in p but adjusted for the value passed to minChange.

See also

Other Time series operations: ts_center(), ts_changeindex(), ts_checkfix(), ts_detrend(), ts_diff(), ts_discrete(), ts_duration(), ts_embed(), ts_integrate(), ts_levels(), ts_peaks(), ts_permtest_block(), ts_permtest_transmat(), ts_rasterize(), ts_sd(), ts_slice(), ts_standardise(), ts_sumorder(), ts_symbolic(), ts_trimfill(), ts_windower()

Author

Fred Hasselman

Examples

# Slopes in white noise?

set.seed(4321)
y <- rnorm(100)
wn <- ts_levels(y)
#> Skipping adjustment by argument minChange...
plot(wn$pred$x,wn$pred$y, type = "l")
lines(wn$pred$p, col = "red3", lwd = 2)

# This is due to the default changeSensitivity of 0.01

wn2 <- ts_slopes(y,changeSensitivity = .1)
#> Skipping adjustment by argument minChange...
lines(wn2$pred$p, col = "steelblue", lwd = 2)