Package 'StructuralDecompose'

Automatic Anomaly detection

Description

Automatic Anomaly detection

Usage

AnomalyDetection(
  timeseries,
  frequency = 52,
  conf_level = 1.5,
  breaks,
  window_len = 14
)

Arguments

timeseries	Given time series
frequency	Timeseries frequency, defaults to 12 points
conf_level	Confidence level for Anomaly detection
breaks	breakpoints identified
window_len	Window length for anomaly detection

Value

the list of anomalies in the time series, along with the time series plot

Examples

AnomalyDetection(timeseries = StructuralDecompose::Nile_dataset[,1], breaks = c(4, 50, 80))

AnomalyDetection(timeseries = runif(n = 50, min = 1, max = 10),  breaks = c(4, 20, 30))

---

Generation of breakpoints

Description

Generation of breakpoints

Usage

BreakPoints(
  timeseries,
  frequency = 52,
  break_algorithm = "strucchange",
  break_level = 0.05
)

Arguments

timeseries	Given time series
frequency	Timeseries frequency, defaults to 12 points
break_algorithm	Breakpoint algorithm to be used. Defaults to strucchange
break_level	Additional parameters for breakpoint algorithm

Value

A list of breakpoints

Examples

BreakPoints(timeseries = seq(100), frequency = 52, break_level = 0.05)
BreakPoints(timeseries = StructuralDecompose::Nile_dataset[,1], frequency = 52)

---

Minimum level length checks

Description

Minimum level length checks

Usage

LevelCheck(timeseries, level_length = 10, breaks)

Arguments

timeseries	Given time series
level_length	Mean distance between two levels
breaks	breakpoints returned

Value

The series cleaned with the minimum level check

Examples

LevelCheck(timeseries = StructuralDecompose::Nile_dataset[,1], breaks = c(1,4,5))

LevelCheck(timeseries = runif(n = 50, min = 1, max = 10), breaks = c(1,4,5))

---

Mean level checks

Description

Mean level checks

Usage

MeanCleaning(timeseries, mean_level = 0.5, breaks, frequency = 52)

Arguments

timeseries	Given time series
mean_level	Mean distance between two levels
breaks	breakpoints returned
frequency	Timeseries frequency, defaults to 12 points

Value

The series cleaned with the mean check

Examples

MeanCleaning(timeseries = StructuralDecompose::Nile_dataset[,1], breaks = c(1,4,5), frequency = 1)

MeanCleaning(timeseries = runif(n = 50, min = 1, max = 10), breaks = c(1,4,5), frequency = 12)

---

Median level checks

Description

Median level checks

Usage

MedianCleaning(timeseries, median_level = 0.5, breaks, frequency = 52)

Arguments

timeseries	Given time series
median_level	Median distance between two levels
breaks	Breaks identified
frequency	Timeseries frequency, defaults to 12 points

Value

The series cleaned with the median check

Examples

MedianCleaning(timeseries = StructuralDecompose::Nile_dataset[,1], breaks = c(1,4,5))

MedianCleaning(timeseries = runif(n = 50, min = 1, max = 10), breaks = c(1,4,5))

---

Nile River Dataset

Description

Nile River Dataset

---

Smoothening of the time series

Description

Smoothening of the time series

Usage

Smoothing(timeseries, frequency = 52, smoothening_algorithm = "lowess", breaks)

Arguments

timeseries	Given time series
frequency	Timeseries frequency, defaults to 12 points
smoothening_algorithm	Smoothening algorithm required
breaks	Breakpoints identified by the previous algorithm
lowess	Lowess smoothener

Value

The smoothened time series

Examples

Smoothing(timeseries = StructuralDecompose::Nile_dataset[,1], breaks = c(4, 50, 80))

Smoothing(timeseries = runif(n = 50, min = 1, max = 10), breaks = c(4, 20, 30))

---

Main decomposition algorithm

Description

Main decomposition algorithm

Usage

StructuralDecompose(
  Data,
  frequency = 12,
  break_algorithm = "strucchange",
  smoothening_algorithm = "lowess",
  break_level = 0.05,
  median_level = 0.5,
  mean_level = 0.5,
  level_length = 12,
  conf_level = 0.5,
  window_len = 12,
  plot = FALSE
)

Arguments

Data	Time series required
frequency	Frequency of the tine series
break_algorithm	breakpoints algorithm used. Defaults to strucchange
smoothening_algorithm	Smoothing algorithm used. Defaults to lowess
break_level	Break level for the breakpoints algorithm
median_level	Average median distance between two level
mean_level	Average mean distance between a group of points near breakpoints
level_length	Minimum number of points required to determine a level
conf_level	Confidence level for Anomaly detection, best to keep this a static value
window_len	Length of the Moving window for Anomaly Detection
plot	True of False indicating if you want the internal plots to be generated

Value

The decomposed time series along with a host of other metrics

Examples

StructuralDecompose(Data = StructuralDecompose::Nile_dataset[,1])

StructuralDecompose(Data = runif(n = 50, min = 1, max = 10))

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