Datasets

The datasets module contains:

• loaders which fetch datasets from data repositories on the internet, and retrieve them as in-memory datasets in sktime compatible formats

• loaders which fetch an individual dataset, usually for illustration purposes

• utilities to write to, and load from, time series specific file formats

Loaders from dataset repositories

These loaders access dataset repositories on the internet and fetch one or multiple datasets from there, individual datasets specifiable as strings.

These loaders can be used to access reference datasets for benchmarking.

Functions to load and write datasets.

load_forecastingdata(name[, ...])	Fetch forecasting datasets from Monash Time Series Forecasting Archive.
load_UCR_UEA_dataset(name[, split, ...])	Load dataset from UCR UEA time series archive.

Individual datasets

These loaders fetch a commonly used individual dataset, usually for illustration purposes.

Single time series

Functions to load and write datasets.

load_airline()	Load the airline univariate time series dataset [1].
load_longley([y_name])	Load the Longley dataset for forecasting with exogenous variables.
load_lynx()	Load the lynx univariate time series dataset for forecasting.
load_macroeconomic()	Load the US Macroeconomic Data [R7ccb796becb7-1].
load_shampoo_sales()	Load the shampoo sales univariate time series dataset for forecasting.
load_solar([start, end, normalise, ...])	Get national solar estimates for GB from Sheffield Solar PV_Live API.
load_uschange([y_name])	Load MTS dataset for forecasting Growth rates of personal consumption and income.

Panels of time series

Functions to load and write datasets.

load_acsf1([split, return_X_y, return_type])	Load dataset on power consumption of typical appliances.
load_arrow_head([split, return_X_y, return_type])	Load the ArrowHead time series classification problem and returns X and y.
load_basic_motions([split, return_X_y, ...])	Load the BasicMotions time series classification problem and returns X and y.
load_gunpoint([split, return_X_y, return_type])	Load the GunPoint time series classification problem and returns X and y.
load_italy_power_demand([split, return_X_y, ...])	Load ItalyPowerDemand time series classification problem.
load_japanese_vowels([split, return_X_y, ...])	Load the JapaneseVowels time series classification problem.
load_macroeconomic()	Load the US Macroeconomic Data [R7ccb796becb7-1].
load_osuleaf([split, return_X_y, return_type])	Load the OSULeaf time series classification problem and returns X and y.

Loading from and writing to files

These utilities load and write from time series specific data formats.

Note: for loading/writing from formats not specific to time series, use common utilities such as pandas.read_csv

Functions to load and write datasets.

load_from_arff_to_dataframe(...[, ...])	Load data from a .arff file into a Pandas DataFrame.
load_from_tsfile(full_file_path_and_name[, ...])	Load time series .ts file into X and (optionally) y.
load_from_tsfile_to_dataframe(...[, ...])	Load data from a .ts file into a Pandas DataFrame.
load_from_ucr_tsv_to_dataframe(...[, ...])	Load data from a .tsv file into a Pandas DataFrame.
load_from_long_to_dataframe(...[, separator])	Load data from a long format file into a Pandas DataFrame.
load_tsf_to_dataframe(full_file_path_and_name)	Convert the contents in a .tsf file into a dataframe.
write_panel_to_tsfile(data, path[, target, ...])	Write an sktime multi-instance dataset to text file in .ts format.
write_dataframe_to_tsfile(data, path[, ...])	Output a dataset in dataframe format to .ts file.
write_ndarray_to_tsfile(data, path[, ...])	Output a dataset in ndarray format to .ts file.
write_tabular_transformation_to_arff(data, ...)	Transform dataset using a tabular transformer and write the result to arff file.
write_results_to_uea_format(estimator_name, ...)	Write the predictions for an experiment in the standard format used by sktime.