ts_datasets.forecast package

Datasets for time series forecasting. Really, these are just time series with no labels of any sort.

ts_datasets.forecast.get_dataset(dataset_name, rootdir=None, **kwargs)

Parameters

dataset_name (str) – the name of the dataset to load, formatted as <name> or <name>_<subset>, e.g. EnergyPower or M4_Hourly
rootdir (Optional[str]) – the directory where the desired dataset is stored. Not required if the package ts_datasets is installed in editable mode, i.e. with flag -e.
kwargs – keyword arguments for the data loader you are trying to load.

Return type

BaseDataset

Returns

the data loader for the desired dataset (and subset) desired

class ts_datasets.forecast.CustomDataset(rootdir, test_frac=0.5, time_col=None, time_unit='s', data_cols=None, index_cols=None)

Bases: BaseDataset

Wrapper to load a custom dataset. Please review the tutorial to get started.

Parameters

rootdir – Filename of a single CSV, or a directory containing many CSVs. Each CSV must contain 1 or more time series.
test_frac – If we don’t find a column “trainval” in the time series, this is the fraction of each time series which we use for testing.
time_col – Name of the column used to index time. We use the first non-index, non-metadata column if none is given.
time_unit – If the time column is numerical, we assume it is a timestamp expressed in this unit.
data_cols – Name of the columns to fetch from the dataset. If None, use all non-time, non-index columns.
index_cols – If a CSV file contains multiple time series, these are the columns used to index those time series. For example, a CSV file may contain time series of sales for many (store, department) pairs. In this case, index_cols may be ["Store", "Dept"]. The values of the index columns will be added to the metadata of the data loader.

property metadata_cols

check_ts_for_metadata(ts, col)

class ts_datasets.forecast.M4(subset='Hourly', rootdir=None)

Bases: BaseDataset

The M4 Competition data is an extended and diverse set of time series to identify the most accurate forecasting method(s) for different types of domains, including Business, financial and economic forecasting, and different type of granularity, including Yearly (23,000 sequences), Quarterly (24,000 sequences), Monthly (48,000 sequences), Weekly(359 sequences), Daily (4,227 sequences) and Hourly (414 sequences) data.