Module Reference¶

functions.
r2
(true, pred)¶ Coefficient of determination (R²)

functions.
rmse
(true, pred)¶ Root mean squared error

functions.
mape
(true, pred)¶ Mean absolute percentage error

functions.
mae
(true, pred)¶ Median absolute error

functions.
pocid
(true, pred)¶ Prediction on change of direction

functions.
periodogram
(x, *args, detrend='diff', **kwargs)¶ Return periodogram of signal x.
Parameters:  x (array_like) – A 1D signal.
 detrend ('diff' or False or int) – Remove trend from x. If int, fit and subtract a polynomial of this order. See also: statsmodels.tsa.detrend.
 kwargs (args,) – As accepted by scipy.signal.periodogram.
Returns:  periods (array_like) – The periods at which the spectral density is calculated.
 pgram (array_like) – Power spectral density of x.

functions.
periodogram_nonequispaced
(times, x, *, freqs=None, period_low=None, period_high=None, n_periods=1000, detrend='linear')¶ Compute the LombScargle periodogram for nonequispaced timeseries.
Parameters:  times (array_like) – Sample times.
 x (array_like) – A 1D signal.
 freqs (array_like, optional) – Angular frequencies for output periodogram.
 period_low (float) – If freqs not provided, the lowest period for which to look for periodicity. Defaults to 5th percentile of time difference between observations.
 period_high (float) – If freqs not provided, the highest period for which to look for periodicity. Defaults to 80th percentile of time difference of observations, or 200*period_low, whichever is larger.
 n_periods (int) – Number of periods between period_low and period_high to try.
 detrend ('diff' or False or int) – Remove trend from x. If int, fit and subtract a polynomial of this order. See also: statsmodels.tsa.detrend.
Returns:  periods (array_like) – The periods at which the spectral density is calculated.
 pgram (array_like) – LombScargle periodogram.
Notes
Read also: https://jakevdp.github.io/blog/2015/06/13/lombscargleinpython/#lombscarglealgorithmsinpython

functions.
autocorrelation
(x, *args, unbiased=True, nlags=None, fft=True, **kwargs)¶ Return autocorrelation function of signal x.
Parameters:  x (array_like) – A 1D signal.
 nlags (int) – The number of lags to calculate the correlation for (default .9*len(x))
 fft (bool) – Compute the ACF via FFT.
 kwargs (args,) – As accepted by statsmodels.tsa.stattools.acf.
Returns:  acf (array) – Autocorrelation function.
 confint (array, optional) – Confidence intervals if alpha kwarg provided.

functions.
partial_autocorrelation
(x, *args, nlags=None, method='ldb', **kwargs)¶ Return partial autocorrelation function (PACF) of signal x.
Parameters:  x (array_like) – A 1D signal.
 nlags (int) – The number of lags to calculate the correlation for (default: min(600, len(x)))
 kwargs (args,) – As accepted by statsmodels.tsa.stattools.pacf.
Returns:  acf (array) – Partioal autocorrelation function.
 confint (optional) – As returned by statsmodels.tsa.stattools.pacf.

functions.
interpolate_timeseries
(data, method='linear', multivariate=False)¶ Return a new Timeseries (Table) with nan values interpolated.
Parameters:  data (Orange.data.Table) – A table to interpolate.
 method (str {'linear', 'cubic', 'nearest', 'mean'}) – The interpolation method to use.
 multivariate (bool) – Whether to perform multivariate (2d) interpolation first. Univariate interpolation of same method is always performed as a final step.
Returns: series – A table with nans in original replaced with interpolated values.
Return type: Timeseries

functions.
seasonal_decompose
(data, model='multiplicative', period=12, *, callback=None)¶ Return table of decomposition components of original features and original features seasonally adjusted.
Parameters:  data (Timeseries) – A table of featres to decompose/adjust.
 model (str {'additive', 'multiplicative'}) – A decompostition model. See: https://en.wikipedia.org/wiki/Decomposition_of_time_series
 period (int) – The period length of season.
 callback (callable) – Optional callback to call (with no parameters) after each iteration.
Returns: table – Table with columns: original series seasonally adjusted, original series’ seasonal components, trend components, and residual components.
Return type: Timeseries

functions.
granger_causality
(data, max_lag=10, alpha=0.05, *, callback=None)¶ Return results of Grangercausality tests.
Parameters:  data (Timeseries) – A table of features to compute Granger causality between.
 max_lag (int) – The maximum lag to compute Grangercausality for.
 alpha (float in (0, 1)) – Confidence of test is 1  alpha.
 callback (callable) – A callback to call in each iteration with ratio of completion.
Returns: res – Each internal list is [lag, antecedent, consequent] where lag is the minimum lag at which antecedent feature in data is Grangercausal for the consequent feature in data.
Return type: list of lists

functions.
moving_transform
(data, spec, fixed_wlen=0)¶ Return data transformed according to spec.
Parameters:  data (Timeseries) – A table with features to transform.
 spec (list of lists) – A list of lists [feature:Variable, window_length:int, function:callable].
 fixed_wlen (int) – If not 0, then window_length in spec is disregarded and this length is used. Also the windows don’t shift by one but instead align themselves side by side.
Returns: transformed – A table of original data its transformations.
Return type: Timeseries

functions.
model_evaluation
(data, models, n_folds, forecast_steps, *, callback=None)¶ Evaluate models on data.
Parameters:  data (Timeseries) – The timeseries to model. Must have a class variable that is used for prediction and scoring.
 models (list) – List of models (objects with fit() and predict() methods) to try.
 n_folds (int) – Number of iterations.
 forecast_steps (int) – Number of forecast steps at each iteraction.
 callback (callable, optional) – Optional argumentless callback to call after each iteration.
Returns: results – A table with horizontal and vertical headers and results. Print it to see it.
Return type: list of lists