Python API Function Summary

tstoolbox.accumulate([input_ts, columns, ...])	Calculate accumulating statistics.
tstoolbox.add_trend(start_offset, end_offset)	Add a trend.
tstoolbox.aggregate([input_ts, groupby, ...])	Take a time series and aggregate to specified frequency.
tstoolbox.calculate_fdc([input_ts, columns, ...])	Return the frequency distribution curve.
tstoolbox.calculate_kde([input_ts, ...])	Return the kernel density estimation (KDE) curve.
tstoolbox.clip([input_ts, start_date, ...])	Return a time-series with values limited to [a_min, a_max].
tstoolbox.convert([input_ts, columns, ...])	Convert values of a time series by applying a factor and offset.
tstoolbox.convert_index(to[, interval, ...])	Convert datetime to/from Julian dates from different epochs.
tstoolbox.convert_index_to_julian([...])	DEPRECATED: Use convert_index instead.
tstoolbox.converttz(fromtz, totz[, ...])	Convert the time zone of the index.
tstoolbox.correlation(lags[, method, ...])	Develop a correlation between time-series and potentially lags.
tstoolbox.createts([input_ts, freq, ...])	Create empty time series, optionally fill with a value.
tstoolbox.date_offset(intervals, offset[, ...])	Apply a date offset to a time-series index.
tstoolbox.date_slice([input_ts, columns, ...])	Print out data to the screen between start_date and end_date.
tstoolbox.describe([input_ts, columns, ...])	Calculate descriptive statistics for the input time-series.
tstoolbox.dtw([input_ts, columns, ...])	Dynamic Time Warping.
tstoolbox.equation(equation_str[, input_ts, ...])	Apply <equation_str> to the time series data.
tstoolbox.ewm_window([input_ts, columns, ...])	Calculate exponential weighted functions.
tstoolbox.expanding_window([input_ts, ...])	Calculate an expanding window statistic.
tstoolbox.fill([input_ts, method, ...])	Fill missing values (NaN) with different methods.
tstoolbox.filter(filter_types, filter_pass)	Apply different filters to the time-series.
tstoolbox.fit(method[, lowess_frac, ...])	Fit model to data.
tstoolbox.gof([obs_col, sim_col, stats, ...])	Calculate goodness of fit statistics between two time-series.
tstoolbox.lag(lags[, input_ts, print_input, ...])	Create a series of lagged time-series.
tstoolbox.normalization([input_ts, columns, ...])	Return the normalization of the time series.
tstoolbox.pca([input_ts, columns, ...])	Return the principal components analysis of the time series.
tstoolbox.pct_change([input_ts, columns, ...])	Return the percent change between times.
tstoolbox.peak_detection([input_ts, ...])	Peak and valley detection.
tstoolbox.pick(columns[, input_ts, ...])	Pick a column or list of columns from input [DEPRECATED].
tstoolbox.plot([input_ts, columns, ...])	Plot data.
tstoolbox.rank([input_ts, columns, ...])	Compute numerical data ranks (1 through n) along axis.
tstoolbox.read(*filenames[, force_freq, ...])	Combine time-series from different sources into single dataset.
tstoolbox.regression(method, x_train_cols, ...)	Regression of one or more time-series or indices to a time-series.
tstoolbox.remove_trend([input_ts, columns, ...])	Remove a trend.
tstoolbox.replace(from_values, to_values[, ...])	Return a time-series replacing values with others.
tstoolbox.rolling_window(statistic[, ...])	Calculate a rolling window statistic.
tstoolbox.stack([input_ts, columns, ...])	Return the stack of the input table.
tstoolbox.stdtozrxp([input_ts, columns, ...])	Print out data to the screen in a WISKI ZRXP format.
tstoolbox.tstopickle(filename[, input_ts, ...])	Pickle the data into a Python pickled file.
tstoolbox.unstack([column_names, input_ts, ...])	Return the unstack of the input table.