Transformer
SFA
Symbolic Fourier Approximation (SFA) Transformer.
- Overview: for each series:
run a sliding window across the series for each window
shorten the series with DFT discretise the shortened series into bins set by MFC form a word from these discrete values
by default SFA produces a single word per series (window_size=0) if a window is used, it forms a histogram of counts of words.
Quickstart
python
from sktime.transformations.dictionary_based import SFA
estimator = SFA(word_length=8, alphabet_size=4, window_size=12, norm=False, binning_method='equi-depth', anova=False, bigrams=False, skip_grams=False, remove_repeat_words=False, levels=1, lower_bounding=True, save_words=False, keep_binning_dft=False, return_pandas_data_series=False, use_fallback_dft=False, typed_dict=False, n_jobs=1)Tags
Capabilities
- Unequal-length series
- Multivariate: Not supported
- Inverse transform: Not supported
- Missing values: Not supported
- Removes missing values: Not supported
- Equalizes series length: Not supported
Properties
- Input typescitype:transform-input
- Series
- Output typescitype:transform-output
- Series
- Label typescitype:transform-labels
- None
- Fit is emptyfit_is_empty
- Yes
- Keeps the time indextransform-returns-same-time-index
- No
- Requires Xrequires_X
- Yes
- Requires yrequires_y
- Yes
- X and y need the same indexX-y-must-have-same-index
- No
Parameters(13)
- word_length: int, default = 8
- length of word to shorten window to (using PAA)
- alphabet_size: int, default = 4
- number of values to discretise each value to
- window_size: int, default = 12
- size of window for sliding. Input series length for whole series transform
- norm: boolean, default = False
- mean normalise words by dropping first fourier coefficient
- binning_method: {“equi-depth”, “equi-width”, “information-gain”, “kmeans”},
default=”equi-depth”
the binning method used to derive the breakpoints.
- anova: boolean, default = False
- If True, the Fourier coefficient selection is done via a one-way ANOVA test. If False, the first Fourier coefficients are selected. Only applicable if labels are given
- bigrams: boolean, default = False
- whether to create bigrams of SFA words
- skip_grams: boolean, default = False
- whether to create skip-grams of SFA words
- remove_repeat_words: boolean, default = False
- whether to use numerosity reduction (default False)
- levels: int, default = 1
- Number of spatial pyramid levels
- save_words: boolean, default = False
- whether to save the words generated for each series (default False)
- return_pandas_data_series: boolean, default = False
- set to true to return Pandas Series as a result of transform. setting to true reduces speed significantly but is required for automatic test.
- n_jobs: int, optional, default = 1
The number of jobs to run in parallel for both
transform.-1means using all processors.
References
[1]
Schäfer, Patrick, and Mikael Högqvist. “SFA: a symbolic fourier approximation
and index for similarity search in high dimensional datasets.” Proceedings of the 15th international conference on extending database technology. 2012.