tsl_initialize function

Transform Raw Time Series Data to Time Series List

Most functions in this package take a time series list (or tsl for short) as main input. A tsl is a list of zoo time series objects (see zoo::zoo()). There is not a formal class for tsl objects, but there are requirements these objects must follow to ensure the stability of the package functionalities (see tsl_diagnose()). These requirements are:

• There are no NA, Inf, -Inf, or NaN cases in the zoo objects (see tsl_count_NA() and tsl_handle_NA()).
• All zoo objects must have at least one common column name to allow time series comparison (see tsl_colnames_get()).
• All zoo objects have a character attribute "name" identifying the object. This attribute is not part of the zoo class, but the package ensures that this attribute is not lost during data manipulations.
• Each element of the time series list is named after the zoo object it contains (see tsl_names_get(), tsl_names_set() and tsl_names_clean()).
• The time series list contains two zoo objects or more.

The function tsl_initialize() (and its alias tsl_init()) is designed to convert the following data structures to a time series list:

• Long data frame: with an ID column to separate time series, and a time column that can be of the classes "Date", "POSIXct", "integer", or "numeric". The resulting zoo objects and list elements are named after the values in the ID column.
• Wide data frame: each column is a time series representing the same variable observed at the same time in different places. Each column is converted to a separate zoo object and renamed.
• List of vectors: an object like list(a = runif(10), b = runif(10)) is converted to a time series list with as many zoo objects as vectors are defined in the original list.
• List of matrices: a list containing matrices, such as list(a = matrix(runif(30), 10, 3), b = matrix(runif(36), 12, 3)).
• List of zoo objects: a list with zoo objects, such as list(a = zoo_simulate(), b = zoo_simulate())

tsl_initialize(
  x = NULL,
  name_column = NULL,
  time_column = NULL,
  lock_step = FALSE
)

tsl_init(x = NULL, name_column = NULL, time_column = NULL, lock_step = FALSE)

Arguments

• x: (required, list or data frame) Matrix or data frame in long format, list of vectors, list of matrices, or list of zoo objects. Default: NULL.
• name_column: (optional, column name) Column naming individual time series. Numeric names are converted to character with the prefix "X". Default: NULL
• time_column: (optional if lock_step = FALSE, and required otherwise, character string) Name of the column representing time, if any. Default: NULL.
• lock_step: (optional, logical) If TRUE, all input sequences are subsetted to their common times according to the values in time_column.

Returns

list of matrices

Examples

#long data frame
#---------------------
data("fagus_dynamics")

#name_column is name
#time column is time
str(fagus_dynamics)

#to tsl
#each group in name_column is a different time series
tsl <- tsl_initialize(
  x = fagus_dynamics,
  name_column = "name",
  time_column = "time"
)

#check validity (no messages or errors if valid)
tsl_diagnose(tsl)

#class of contained objects
lapply(X = tsl, FUN = class)

#get list and zoo names (between double quotes)
tsl_names_get(
  tsl = tsl,
  zoo = TRUE
  )

#plot tsl
if(interactive()){
  tsl_plot(tsl)
}

#list of zoo objects
#--------------------
x <- zoo_simulate()
y <- zoo_simulate()

tsl <- tsl_initialize(
  x = list(
    x = x,
    y = y
  )
)

#plot
if(interactive()){
  tsl_plot(tsl)
}

#wide data frame
#--------------------
#wide data frame
#each column is same variable in different places
df <- stats::reshape(
  data = fagus_dynamics[, c(
    "name",
    "time",
    "evi"
  )],
  timevar = "name",
  idvar = "time",
  direction = "wide",
  sep = "_"
)

str(df)

#to tsl
#key assumptions:
#all columns but "time" represent
#the same variable in different places
#all time series are of the same length
tsl <- tsl_initialize(
  x = df,
  time_column = "time"
  )

#colnames are forced to be the same...
tsl_colnames_get(tsl)

#...but can be changed
tsl <- tsl_colnames_set(
  tsl = tsl,
  names = "evi"
)
tsl_colnames_get(tsl)

#plot
if(interactive()){
  tsl_plot(tsl)
}

#list of vectors
#---------------------
#create list of vectors
vector_list <- list(
  a = cumsum(stats::rnorm(n = 50)),
  b = cumsum(stats::rnorm(n = 70)),
  c = cumsum(stats::rnorm(n = 20))
)

#to tsl
#key assumptions:
#all vectors represent the same variable
#in different places
#time series can be of different lengths
#no time column, integer indices are used as time
tsl <- tsl_initialize(
  x = vector_list
)

#plot
if(interactive()){
  tsl_plot(tsl)
}

#list of matrices
#-------------------------
#create list of matrices
matrix_list <- list(
  a = matrix(runif(30), nrow = 10, ncol = 3),
  b = matrix(runif(80), nrow = 20, ncol = 4)
)

#to tsl
#key assumptions:
#each matrix represents a multivariate time series
#in a different place
#all multivariate time series have the same columns
#no time column, integer indices are used as time
tsl <- tsl_initialize(
  x = matrix_list
)

#check column names
tsl_colnames_get(tsl = tsl)

#remove exclusive column
tsl <- tsl_subset(
  tsl = tsl,
  shared_cols = TRUE
  )
tsl_colnames_get(tsl = tsl)

#plot
if(interactive()){
  tsl_plot(tsl)
}

#list of zoo objects
#-------------------------
zoo_list <- list(
  a = zoo_simulate(),
  b = zoo_simulate()
)

#looks like a time series list! But...
tsl_diagnose(tsl = zoo_list)

#let's set the names
zoo_list <- tsl_names_set(tsl = zoo_list)

#check again: it's now a valid time series list
tsl_diagnose(tsl = zoo_list)

#to do all this in one go:
tsl <- tsl_initialize(
  x = list(
    a = zoo_simulate(),
    b = zoo_simulate()
  )
)

#plot
if(interactive()){
  tsl_plot(tsl)
}