Patterns¶

Note

• M = number of time series

• N = length of time series

• R = rate of missing values chosen by the user (%); default = 0.2

• S = offset in the beginning of the series (%); default = 0.1

• W = R * N

Note

• M ∈ [1%, max]; R ∈ [1%, max]

• The size of a single missing block varies between 1% and (100 - S)% of N.

• The starting position is the same and begins at S and progresses until W is reached, affecting the first series from the top up to M% of the dataset.

Note

• M ∈ [1, max]; R ∈ [1%, max]

• The size of a single missing block varies between 1% and (100 - S)% of N.

• The starting position of the first missing block begins at S.

• Each subsequent missing block starts immediately after the previous one ends, continuing this pattern until the limit of the dataset or N is reached.

Note

• M ∈ [1, max]; R ∈ [1%, max]

• The size of a single missing block varies between 1% and (100 - S)% of N.

• The starting position of the first missing block begins at S.

• Each subsequent missing block starts after the previous one ends, but with a shift back of X%, creating an overlap.

• This pattern continues until the limit or N is reached.

Note

• M ∈ [1%, max]; R ∈ [1%, max]

• The size of a single missing block varies between 1% and (100 - S)% of N.

• The starting position is randomly shifted by adding a random value to S, then progresses until W `` is reached, affecting the first series from the top up to ``M% of the dataset.

Note

• M ∈ [1%, max]; R ∈ [1%, max]

• Data blocks of the same size are removed from arbitrary series at a random position between S and N, until a total of W per series is missing.

Note

• M ∈ [1%, max]; R ∈ [1%, max]

• Data is removed following a distribution given by the user for every values of N, affecting the first series from the top up to M% of the dataset.