ts_datasets.anomaly package

Datasets for time series anomaly detection (TSAD). All the time series in these datasets have anomaly labels.

ts_datasets.anomaly.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. IOPsCompetition or NAB_realAWSCloudwatch

  • 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

TSADBaseDataset

Returns

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

class ts_datasets.anomaly.TSADBaseDataset

Bases: BaseDataset

Base dataset class for storing time series intended for anomaly detection.

Each dataset supports the following features:

  1. __getitem__: you may call ts, metadata = dataset[i]. ts is a time-indexed pandas DataFrame, with each column representing a different variable (in the case of multivariate time series). metadata is a dict or pd.DataFrame with the same index as ts, with different keys indicating different dataset-specific metadata (train/test split, anomaly labels, etc.) for each timestamp.

  2. __len__: Calling len(dataset) will return the number of time series in the dataset.

  3. __iter__: You may iterate over the pandas representations of the time series in the dataset with for ts, metadata in dataset: ...

Note

For each time series, the metadata will always have the key trainval, which is a pd.Series of bool indicating whether each timestamp of the time series should be training/validation (if True) or testing (if False).

Note

For each time series, the metadata will always have the key anomaly, which is a pd.Series of bool indicating whether each timestamp is anomalous.

property max_lead_sec

The maximum number of seconds an anomaly may be detected early, for this dataset. None signifies no early detections allowed, or that the user may override this value with something better suited for their purposes.

property max_lag_sec

The maximum number of seconds after the start of an anomaly, that we consider detections to be accurate (and not ignored for being too late). None signifies that any detection in the window is acceptable, or that the user may override this value with something better suited for their purposes.

describe()
class ts_datasets.anomaly.CustomAnomalyDataset(rootdir, test_frac=0.5, assume_no_anomaly=False, time_col=None, time_unit='s', data_cols=None, index_cols=None)

Bases: CustomDataset, TSADBaseDataset

Wrapper to load a custom dataset for anomaly detection. 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.

  • assume_no_anomaly – If we don’t find a column “anomaly” in the time series, we assume there are no anomalies in the data if this value is True, and we throw an exception if this value is False.

  • time_col – Name of the column used to index time. We use the first non-index, non-metadata column if none is given.

  • data_cols – Name of the columns to fetch from the dataset. If None, use all non-time, non-index columns.

  • time_unit – If the time column is numerical, we assume it is a timestamp expressed in this unit.

  • 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.anomaly.IOpsCompetition(rootdir=None)

Bases: TSADBaseDataset

Wrapper to load the dataset used for the final round of the IOPs competition (http://iops.ai/competition_detail/?competition_id=5).

The dataset contains 29 time series of KPIs gathered from large tech companies (Alibaba, Sogou, Tencent, Baidu, and eBay). These time series are sampled at either 1min or 5min intervals, and are split into train and test sections.

Note that the original competition prohibited algorithms which directly hard-coded the KPI ID to set model parameters. So training a new model for each time series was against competition rules. They did, however, allow algorithms which analyzed each time series (in an automated way), and used the results of that automated analysis to perform algorithm/model selection.

Parameters

rootdir – The root directory at which the dataset can be found.

property max_lag_sec

The IOps competition allows anomalies to be detected up to 35min after they start. We are currently not using this, but we are leaving the override here as a placeholder, if we want to change it later.

class ts_datasets.anomaly.NAB(subset='all', rootdir=None)

Bases: TSADBaseDataset

Wrapper to load datasets found in the Numenta Anomaly Benchmark (https://github.com/numenta/NAB).

The NAB contains a range of datasets and are categorized by their domains.

Parameters

  • subset – One of the elements in subsets.

  • rootdir – The root directory at which the dataset can be found.

valid_subsets = ['all', 'artificial', 'artificialWithAnomaly', 'realAWSCloudwatch', 'realAdExchange', 'realKnownCause', 'realTraffic', 'realTweets']
static load_labels(datafile, label_list, freq)
property max_lead_sec

The anomalies in the NAB dataset are already windows which permit early detection. So we explicitly disallow any earlier detection.

download(rootdir, subsets)
class ts_datasets.anomaly.Synthetic(subset='anomaly', rootdir=None)

Bases: TSADBaseDataset

Wrapper to load a sythetically generated dataset. The dataset was generated using three base time series, each of which was separately injected with shocks, spikes, dips and level shifts, making a total of 15 time series (including the base time series without anomalies). Subsets can are defined by the base time series used (“horizontal”, “seasonal”, “upward_downward”), or the type of injected anomaly (“shock”, “spike”, “dip”, “level”). The “anomaly” subset refers to all times series with injected anomalies (12) while “base” refers to all time series without them (3).

base_ts_subsets = ['horizontal', 'seasonal', 'upward_downward']
anomaly_subsets = ['shock', 'spike', 'dip', 'level', 'trend']
valid_subsets = ['anomaly', 'all', 'base', 'horizontal', 'seasonal', 'upward_downward', 'shock', 'spike', 'dip', 'level', 'trend']
class ts_datasets.anomaly.UCR(rootdir=None)

Bases: TSADBaseDataset

Data loader for the Hexagon ML/UC Riverside Time Series Anomaly Archive.

See here for details.

Hoang Anh Dau, Eamonn Keogh, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Yanping Chen, Bing Hu, Nurjahan Begum, Anthony Bagnall , Abdullah Mueen, Gustavo Batista, & Hexagon-ML (2019). The UCR Time Series Classification Archive. URL https://www.cs.ucr.edu/~eamonn/time_series_data_2018/

download(rootdir)
class ts_datasets.anomaly.SMD(subset='all', rootdir=None)

Bases: TSADBaseDataset

The Server Machine Dataset (SMD) is a new 5-week-long dataset from a large Internet company collected and made publicly available. It contains data from 28 server machines and each machine is monitored by 33 metrics. SMD is divided into training set and testing set of equal size.

filename = 'ServerMachineDataset'
url = 'https://www.dropbox.com/s/x53ph5cru62kv0f/ServerMachineDataset.tar.gz?dl=1'
valid_subsets = ['machine-1-1', 'machine-1-2', 'machine-1-3', 'machine-1-4', 'machine-1-5', 'machine-1-6', 'machine-1-7', 'machine-1-8', 'machine-2-1', 'machine-2-2', 'machine-2-3', 'machine-2-4', 'machine-2-5', 'machine-2-6', 'machine-2-7', 'machine-2-8', 'machine-2-9', 'machine-3-1', 'machine-3-2', 'machine-3-3', 'machine-3-4', 'machine-3-5', 'machine-3-6', 'machine-3-7', 'machine-3-8', 'machine-3-9', 'machine-3-10', 'machine-3-11']
class ts_datasets.anomaly.SMAP(subset=None, rootdir=None)

Bases: TSADBaseDataset

Soil Moisture Active Passive (SMAP) satellite and Mars Science Laboratory (MSL) rover Datasets. SMAP and MSL are two realworld public datasets, which are two real-world datasets expert-labeled by NASA.

url = 'https://www.dropbox.com/s/uv9ojw353qwzqht/SMAP.tar.gz?dl=1'
class ts_datasets.anomaly.MSL(subset=None, rootdir=None)

Bases: TSADBaseDataset

Soil Moisture Active Passive (SMAP) satellite and Mars Science Laboratory (MSL) rover Datasets. SMAP and MSL are two realworld public datasets, which are two real-world datasets expert-labeled by NASA.

url = 'https://www.dropbox.com/s/uv9ojw353qwzqht/SMAP.tar.gz?dl=1'