Python API Function Summary

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