Description
The Symbolic Aggregate approXimation function transforms original time series data into symbolic strings, which are more suitable for additional types of manipulation, because of their smaller size and the relative ease with which patterns can be identified and compared. Input and output formats allow it to supply data to the Shapelet Functions.
Usage
td_sax_mle (
data = NULL,
data.partition.column = NULL,
data.order.column = NULL,
meanstats.data = NULL,
stdevstats.data = NULL,
value.columns = NULL,
time.column = NULL,
window.type = "global",
output = "string",
mean = NULL,
st.dev = NULL,
window.size = NULL,
output.frequency = 1,
points.persymbol = 1,
symbols.perwindow = NULL,
alphabet.size = 4,
bitmap.level = 2,
print.stats = FALSE,
accumulate = NULL,
data.sequence.column = NULL,
meanstats.data.sequence.column = NULL,
stdevstats.data.sequence.column = NULL,
meanstats.data.partition.column = NULL,
stdevstats.data.partition.column = NULL,
meanstats.data.order.column = NULL,
stdevstats.data.order.column = NULL
)
Arguments
data |
Required Argument. |
data.partition.column |
Required Argument. |
data.order.column |
Required Argument. |
meanstats.data |
Optional Argument. |
meanstats.data.partition.column |
Optional Argument. Required if "meanstats.data" is specified. |
meanstats.data.order.column |
Optional Argument. |
stdevstats.data |
Optional Argument. |
stdevstats.data.partition.column |
Optional Argument. Required if "stdevstats.data" is specified. |
stdevstats.data.order.column |
Optional Argument. |
value.columns |
Required Argument. |
time.column |
Optional Argument. |
window.type |
Optional Argument.
|
output |
Optional Argument.
|
mean |
Optional Argument. |
st.dev |
Optional Argument. |
window.size |
Optional Argument. |
output.frequency |
Optional Argument. |
points.persymbol |
Optional Argument. |
symbols.perwindow |
Optional Argument. |
alphabet.size |
Optional Argument. |
bitmap.level |
Optional Argument. |
print.stats |
Optional Argument. |
accumulate |
Optional Argument. |
data.sequence.column |
Optional Argument. |
meanstats.data.sequence.column |
Optional Argument. |
stdevstats.data.sequence.column |
Optional Argument. |
Value
Function returns an object of class "td_sax_mle" which is a named
list containing object of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator
using the name: result.
Examples
# Get the current context/connection
con <- td_get_context()$connection
# Load example data.
loadExampleData("sax_example", "finance_data3")
# Create object(s) of class "tbl_teradata".
finance_data3 <- tbl(con, "finance_data3")
# Example 1: This example uses "window.type" as global and default output value.
td_sax_out <- td_sax_mle(data = finance_data3,
data.partition.column = c("id"),
data.order.column = c("period"),
value.columns = c("expenditure","income","investment"),
time.column = "period",
window.type = "global",
print.stats = TRUE,
accumulate = c("id")
)
# Example 2: This example uses "window.type" as sliding and default output value.
# "window.size" should also be specified when "window.type" is set as sliding.
td_sax_out2 <- td_sax_mle(data = finance_data3,
data.partition.column = c("id"),
data.order.column = c("period"),
value.columns = c("expenditure"),
time.column = "period",
window.type = "sliding",
window.size = 20,
print.stats = TRUE,
accumulate = c("id")
)
# Example 3: This example uses a the multiple-input version, where the
# mean and standard deviation statistics are applied globally with the
# meanstats tbl_teradata and the stdevstats tbl_teradata.
meanstats <- tbl(con, "finance_data3") %>% group_by(id) %>%
summarize(expenditure = mean(expenditure, na.rm = TRUE),
income = mean(income, na.rm = TRUE),
investment = mean(investment, na.rm = TRUE))
stdevstats <- tbl(con, "finance_data3") %>% group_by(id) %>%
summarize(expenditure = sd(expenditure, na.rm = TRUE),
income = sd(income, na.rm = TRUE),
investment = sd(investment, na.rm = TRUE))
td_sax_out3 <- td_sax_mle(data = finance_data3,
data.partition.column = c("id"),
data.order.column = c("id"),
meanstats.data = meanstats,
meanstats.data.partition.column = c("id"),
stdevstats.data = stdevstats,
stdevstats.data.partition.column = c("id"),
value.columns = c("expenditure","income","investment"),
time.column = "period",
window.type = "global",
accumulate = c("id")
)