from __future__ import annotations
import copy
import itertools
import logging
from collections.abc import Iterable
from typing import Any, List, Optional, Tuple, Type
import numpy as np
import pandas as pd
from joblib.parallel import Parallel, delayed
from pandas.core.internals import ArrayManager, BlockManager
from autogluon.common.loaders import load_pd
logger = logging.getLogger(__name__)
ITEMID = "item_id"
TIMESTAMP = "timestamp"
IRREGULAR_TIME_INDEX_FREQSTR = "IRREG"
[docs]class TimeSeriesDataFrame(pd.DataFrame):
"""A collection of univariate time series, where each row is identified by an (``item_id``, ``timestamp``) pair.
For example, a time series data frame could represent the daily sales of a collection of products, where each
``item_id`` corresponds to a product and ``timestamp`` corresponds to the day of the record.
Parameters
----------
data : Any
Time series data to construct a ``TimeSeriesDataFrame``. The class currently supports four input formats.
1. Time series data in a pandas DataFrame format without multi-index. For example::
item_id timestamp target
0 0 2019-01-01 0
1 0 2019-01-02 1
2 0 2019-01-03 2
3 1 2019-01-01 3
4 1 2019-01-02 4
5 1 2019-01-03 5
6 2 2019-01-01 6
7 2 2019-01-02 7
8 2 2019-01-03 8
2. Time series data in pandas DataFrame format with multi-index on ``item_id`` and ``timestamp``. For example::
target
item_id timestamp
0 2019-01-01 0
2019-01-02 1
2019-01-03 2
1 2019-01-01 3
2019-01-02 4
2019-01-03 5
2 2019-01-01 6
2019-01-02 7
2019-01-03 8
3. Path to a data file in CSV or Parquet format. The file must contain columns ``item_id`` and ``timestamp``, as well as columns with time series values. This is similar to Option 1 above (pandas DataFrame format without multi-index). Both remote (e.g., S3) and local paths are accepted.
4. Time series data in Iterable format. For example::
iterable_dataset = [
{"target": [0, 1, 2], "start": pd.Period("01-01-2019", freq='D')},
{"target": [3, 4, 5], "start": pd.Period("01-01-2019", freq='D')},
{"target": [6, 7, 8], "start": pd.Period("01-01-2019", freq='D')}
]
static_features : Optional[pd.DataFrame]
An optional data frame describing the metadata attributes of individual items in the item index. These
may be categorical or real valued attributes for each item. For example, if the item index refers to
time series data of individual households, static features may refer to time-independent demographic
features. When provided during ``fit``, the ``TimeSeriesPredictor`` expects the same metadata to be available
during prediction time. When provided, the index of the ``static_features`` index must match the item index
of the ``TimeSeriesDataFrame``.
``TimeSeriesDataFrame`` will ensure consistency of static features during serialization/deserialization,
copy and slice operations although these features should be considered experimental.
Attributes
----------
freq : str
A pandas-compatible string describing the frequency of the time series. For example ``"D"`` for daily data,
``"H"`` for hourly data, etc. This attribute is determined automatically based on the timestamps. For the full
list of possible values, see
https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases
num_items : int
Number of items (time series) in the data set.
item_ids : pd.Index
List of unique time series IDs contained in the data set.
"""
DUMMY_INDEX_START_TIME = pd.Timestamp("1900-01-01 00:00:00")
index: pd.MultiIndex
_metadata = ["_static_features", "_cached_freq"]
[docs] def __init__(self, data: Any, static_features: Optional[pd.DataFrame] = None, *args, **kwargs):
if isinstance(data, (BlockManager, ArrayManager)):
# necessary for copy constructor to work. see _constructor
# and pandas.DataFrame
pass
elif isinstance(data, pd.DataFrame):
if isinstance(data.index, pd.MultiIndex):
self._validate_multi_index_data_frame(data)
else:
data = self._construct_pandas_frame_from_data_frame(data)
elif isinstance(data, str):
data = self._load_data_frame_from_file(data)
elif isinstance(data, Iterable):
data = self._construct_pandas_frame_from_iterable_dataset(data)
else:
raise ValueError("Data input type not recognized, must be DataFrame, iterable or string.")
super().__init__(data=data, *args, **kwargs)
self._static_features: Optional[pd.DataFrame] = None
if static_features is not None:
self.static_features = static_features
# internal value for cached frequency values that are inferred. corresponds to either a
# pandas-compatible frequency string, the value IRREGULAR_TIME_INDEX_FREQSTR that signals
# the time series have irregular timestamps (in which case tsdf.freq returns None), or None
# if inference was not yet performed.
self._cached_freq: Optional[str] = None
@property
def _constructor(self) -> Type[TimeSeriesDataFrame]:
return TimeSeriesDataFrame
@property
def item_ids(self) -> pd.Index:
return self.index.unique(level=ITEMID)
@property
def static_features(self):
return self._static_features
@static_features.setter
def static_features(self, value: Optional[pd.DataFrame]):
# if the current item index is not a multiindex, then we are dealing with a single
# item slice. this should only happen when the user explicitly requests only a
# single item or during `slice_by_timestep`. In this case we do not set static features
if not isinstance(self.index, pd.MultiIndex):
return
if value is not None:
if isinstance(value, pd.Series):
value = value.to_frame()
if not isinstance(value, pd.DataFrame):
raise ValueError(f"static_features must be a pandas DataFrame (received object of type {type(value)})")
missing_item_ids = self.item_ids.difference(value.index)
if len(missing_item_ids) > 0:
raise ValueError(
f"Following item_ids are missing from the index of static_features: {missing_item_ids.to_list()}"
)
# if provided static features are a strict superset of the item index, we take a subset to ensure consistency
if len(value.index.difference(self.item_ids)) > 0:
value = value.loc[self.item_ids]
# Avoid modifying static features inplace
value = value.copy()
# TODO: If item_id is a column, set this column as index
if value.index.name != ITEMID:
value.index.rename(ITEMID, inplace=True)
self._static_features = value
@property
def freq(self):
if self._cached_freq is not None and self._cached_freq == IRREGULAR_TIME_INDEX_FREQSTR:
return None # irregularly sampled time series
elif self._cached_freq:
return self._cached_freq
def get_freq(series):
return series.index.freq or series.index.inferred_freq
# check the frequencies of the first 100 items to see if frequencies are consistent and
# can be inferred
freq_for_each_series = [get_freq(self.loc[idx]) for idx in self.item_ids[:100]]
freq = freq_for_each_series[0]
if len(set(freq_for_each_series)) > 1 or freq is None:
self._cached_freq = IRREGULAR_TIME_INDEX_FREQSTR
return None
freq = freq.freqstr if isinstance(freq, pd._libs.tslibs.BaseOffset) else freq
self._cached_freq = freq
return freq
@property
def num_items(self):
return len(self.item_ids)
[docs] def num_timesteps_per_item(self) -> pd.Series:
"""Length of each time series in the dataframe."""
return self.groupby(level=ITEMID, sort=False).size()
@classmethod
def _validate_iterable(cls, data: Iterable):
if not isinstance(data, Iterable):
raise ValueError("data must be of type Iterable.")
first = next(iter(data), None)
if first is None:
raise ValueError("data has no time-series.")
for i, ts in enumerate(itertools.chain([first], data)):
if not isinstance(ts, dict):
raise ValueError(f"{i}'th time-series in data must be a dict, got{type(ts)}")
if not ("target" in ts and "start" in ts):
raise ValueError(f"{i}'th time-series in data must have 'target' and 'start', got{ts.keys()}")
if not isinstance(ts["start"], (pd.Timestamp, pd.Period)) or ts["start"].freq is None:
raise ValueError(
f"{i}'th time-series must have timestamp as 'start' with freq specified, got {ts['start']}"
)
@classmethod
def _validate_data_frame(cls, df: pd.DataFrame):
if not isinstance(df, pd.DataFrame):
raise ValueError(f"data must be a pd.DataFrame, got {type(df)}")
if ITEMID not in df.columns:
raise ValueError(f"data must have a `{ITEMID}` column")
if TIMESTAMP not in df.columns:
raise ValueError(f"data must have a `{TIMESTAMP}` column")
if df[ITEMID].isnull().any():
raise ValueError(f"`{ITEMID}` column can not have nan")
if df[TIMESTAMP].isnull().any():
raise ValueError(f"`{TIMESTAMP}` column can not have nan")
if not df[TIMESTAMP].dtype == "datetime64[ns]":
raise ValueError(f"for {TIMESTAMP}, the only pandas dtype allowed is `datetime64[ns]`.")
item_id_column = df[ITEMID]
# workaround for pd.api.types.is_string_dtype issue https://github.com/pandas-dev/pandas/issues/15585
item_id_is_string = (item_id_column == item_id_column.astype(str)).all()
item_id_is_int = pd.api.types.is_integer_dtype(item_id_column)
if not (item_id_is_string or item_id_is_int):
raise ValueError(f"all entries in column `{ITEMID}` must be of integer or string dtype")
@classmethod
def _validate_multi_index_data_frame(cls, data: pd.DataFrame):
"""Validate a multi-index pd.DataFrame can be converted to TimeSeriesDataFrame
Parameters
----------
data: pd.DataFrame
a data frame in pd.DataFrame format.
"""
if not isinstance(data, pd.DataFrame):
raise ValueError(f"data must be a pd.DataFrame, got {type(data)}")
if not isinstance(data.index, pd.MultiIndex):
raise ValueError(f"data must have pd.MultiIndex, got {type(data.index)}")
if not data.index.dtypes.array[1] == "datetime64[ns]":
raise ValueError(f"for {TIMESTAMP}, the only pandas dtype allowed is `datetime64[ns]`.")
if not data.index.names == (f"{ITEMID}", f"{TIMESTAMP}"):
raise ValueError(f"data must have index names as ('{ITEMID}', '{TIMESTAMP}'), got {data.index.names}")
item_id_index = data.index.get_level_values(level=ITEMID)
# workaround for pd.api.types.is_string_dtype issue https://github.com/pandas-dev/pandas/issues/15585
item_id_is_string = (item_id_index == item_id_index.astype(str)).all()
item_id_is_int = pd.api.types.is_integer_dtype(item_id_index)
if not (item_id_is_string or item_id_is_int):
raise ValueError(f"all entries in index `{ITEMID}` must be of integer or string dtype")
@classmethod
def _construct_pandas_frame_from_iterable_dataset(
cls, iterable_dataset: Iterable, num_cpus: int = -1
) -> pd.DataFrame:
def load_single_item(item_id: int, ts: dict) -> pd.DataFrame:
start_timestamp = ts["start"]
freq = start_timestamp.freq
if isinstance(start_timestamp, pd.Period):
start_timestamp = start_timestamp.to_timestamp(how="S")
target = ts["target"]
datetime_index = tuple(pd.date_range(start_timestamp, periods=len(target), freq=freq))
idx = pd.MultiIndex.from_product([(item_id,), datetime_index], names=[ITEMID, TIMESTAMP])
return pd.Series(target, name="target", index=idx).to_frame()
cls._validate_iterable(iterable_dataset)
all_ts = Parallel(n_jobs=num_cpus)(
delayed(load_single_item)(item_id, ts) for item_id, ts in enumerate(iterable_dataset)
)
return pd.concat(all_ts)
[docs] @classmethod
def from_iterable_dataset(cls, iterable_dataset: Iterable, num_cpus: int = -1) -> pd.DataFrame:
"""Construct a ``TimeSeriesDataFrame`` from an Iterable of dictionaries each of which
represent a single time series.
This function also offers compatibility with GluonTS data sets, see
https://ts.gluon.ai/_modules/gluonts/dataset/common.html#ListDataset.
Parameters
----------
iterable_dataset: Iterable
An iterator over dictionaries, each with a ``target`` field specifying the value of the
(univariate) time series, and a ``start`` field that features a pandas Timestamp with features.
Example::
iterable_dataset = [
{"target": [0, 1, 2], "start": pd.Timestamp("01-01-2019", freq='D')},
{"target": [3, 4, 5], "start": pd.Timestamp("01-01-2019", freq='D')},
{"target": [6, 7, 8], "start": pd.Timestamp("01-01-2019", freq='D')}
]
num_cpus : int, default = -1
Number of CPU cores used to process the iterable dataset in parallel. Set to -1 to use all cores.
Returns
-------
ts_df: TimeSeriesDataFrame
A data frame in TimeSeriesDataFrame format.
"""
return cls(cls._construct_pandas_frame_from_iterable_dataset(iterable_dataset, num_cpus=num_cpus))
@classmethod
def _load_data_frame_from_file(cls, path: str) -> pd.DataFrame:
df = load_pd.load(path)
return cls._construct_pandas_frame_from_data_frame(df)
@classmethod
def _construct_pandas_frame_from_data_frame(
cls,
df: pd.DataFrame,
id_column: Optional[str] = None,
timestamp_column: Optional[str] = None,
) -> pd.DataFrame:
df = df.copy()
if id_column is not None:
assert id_column in df.columns, f"Column '{id_column}' not found!"
if id_column != ITEMID and ITEMID in df.columns:
logger.warning(f"Renaming existing column '{ITEMID}' -> '__{ITEMID}' to avoid name collisions.")
df.rename(columns={ITEMID: "__" + ITEMID}, inplace=True)
df.rename(columns={id_column: ITEMID}, inplace=True)
if timestamp_column is not None:
assert timestamp_column in df.columns, f"Column '{timestamp_column}' not found!"
if timestamp_column != TIMESTAMP and TIMESTAMP in df.columns:
logger.warning(f"Renaming existing column '{TIMESTAMP}' -> '__{TIMESTAMP}' to avoid name collisions.")
df.rename(columns={TIMESTAMP: "__" + TIMESTAMP}, inplace=True)
df.rename(columns={timestamp_column: TIMESTAMP}, inplace=True)
if TIMESTAMP in df.columns:
df[TIMESTAMP] = pd.to_datetime(df[TIMESTAMP])
cls._validate_data_frame(df)
return df.set_index([ITEMID, TIMESTAMP])
[docs] @classmethod
def from_path(
cls,
path: str,
id_column: Optional[str] = None,
timestamp_column: Optional[str] = None,
) -> TimeSeriesDataFrame:
"""Construct a ``TimeSeriesDataFrame`` from a CSV or Parquet file.
Parameters
----------
path : str
Path to a local or remote (e.g., S3) file containing the time series data in CSV or Parquet format.
Example file contents::
item_id,timestamp,target
0,2019-01-01,0
0,2019-01-02,1
0,2019-01-03,2
1,2019-01-01,3
1,2019-01-02,4
1,2019-01-03,5
2,2019-01-01,6
2,2019-01-02,7
2,2019-01-03,8
id_column: str
Name of the 'item_id' column if column name is different
timestamp_column: str
Name of the 'timestamp' column if column name is different
Returns
-------
ts_df: TimeSeriesDataFrame
A data frame in TimeSeriesDataFrame format.
"""
df = load_pd.load(path)
return cls.from_data_frame(df, id_column=id_column, timestamp_column=timestamp_column)
[docs] @classmethod
def from_data_frame(
cls,
df: pd.DataFrame,
id_column: Optional[str] = None,
timestamp_column: Optional[str] = None,
) -> TimeSeriesDataFrame:
"""Construct a ``TimeSeriesDataFrame`` from a pandas DataFrame.
Parameters
----------
df: pd.DataFrame
A pd.DataFrame with 'item_id' and 'timestamp' as columns. For example:
.. code-block::
item_id timestamp target
0 0 2019-01-01 0
1 0 2019-01-02 1
2 0 2019-01-03 2
3 1 2019-01-01 3
4 1 2019-01-02 4
5 1 2019-01-03 5
6 2 2019-01-01 6
7 2 2019-01-02 7
8 2 2019-01-03 8
id_column: str
Name of the 'item_id' column if column name is different
timestamp_column: str
Name of the 'timestamp' column if column name is different
Returns
-------
ts_df: TimeSeriesDataFrame
A data frame in TimeSeriesDataFrame format.
"""
return cls(
cls._construct_pandas_frame_from_data_frame(df, id_column=id_column, timestamp_column=timestamp_column)
)
[docs] def copy(self: TimeSeriesDataFrame, deep: bool = True) -> pd.DataFrame: # noqa
obj = super().copy(deep=deep)
# also perform a deep copy for static features
if deep:
for k in obj._metadata:
setattr(obj, k, copy.deepcopy(getattr(obj, k)))
return obj
def __finalize__( # noqa
self: TimeSeriesDataFrame, other, method: Optional[str] = None, **kwargs
) -> TimeSeriesDataFrame:
super().__finalize__(other=other, method=method, **kwargs)
# when finalizing the copy/slice operation, we use the property setter to stay consistent
# with the item index
if hasattr(other, "_static_features"):
self.static_features = other._static_features
if hasattr(other, "_cached_freq"):
self._cached_freq = other._cached_freq
return self
[docs] def split_by_time(self, cutoff_time: pd.Timestamp) -> Tuple[TimeSeriesDataFrame, TimeSeriesDataFrame]:
"""Split dataframe to two different ``TimeSeriesDataFrame`` s before and after a certain ``cutoff_time``.
Parameters
----------
cutoff_time: pd.Timestamp
The time to split the current data frame into two data frames.
Returns
-------
data_before: TimeSeriesDataFrame
Data frame containing time series before the ``cutoff_time`` (exclude ``cutoff_time``).
data_after: TimeSeriesDataFrame
Data frame containing time series after the ``cutoff_time`` (include ``cutoff_time``).
"""
nanosecond_before_cutoff = cutoff_time - pd.Timedelta(nanoseconds=1)
data_before = self.loc[(slice(None), slice(None, nanosecond_before_cutoff)), :]
data_after = self.loc[(slice(None), slice(cutoff_time, None)), :]
before = TimeSeriesDataFrame(data_before, static_features=self.static_features)
after = TimeSeriesDataFrame(data_after, static_features=self.static_features)
before._cached_freq = self._cached_freq
after._cached_freq = self._cached_freq
return before, after
[docs] def slice_by_timestep(
self, start_index: Optional[int] = None, end_index: Optional[int] = None
) -> TimeSeriesDataFrame:
"""Select a subsequence from each time series between start (inclusive) and end (exclusive) indices.
This operation is equivalent to selecting a slice ``[start_index : end_index]`` from each time series, and then
combining these slices into a new ``TimeSeriesDataFrame``. See examples below.
Returns a copy of the original data. This is useful for constructing holdout sets for validation.
Parameters
----------
start_index : int or None
Start index (inclusive) of the slice for each time series.
Negative values are counted from the end of each time series.
When set to None, the slice starts from the beginning of each time series.
end_index : int or None
End index (exclusive) of the slice for each time series.
Negative values are counted from the end of each time series.
When set to None, the slice includes the end of each time series.
Returns
-------
ts_df : TimeSeriesDataFrame
A new time series dataframe containing entries of the original time series between start and end indices.
Examples
--------
>>> print(ts_dataframe)
target
item_id timestamp
0 2019-01-01 0
2019-01-02 1
2019-01-03 2
1 2019-01-02 3
2019-01-03 4
2019-01-04 5
2 2019-01-03 6
2019-01-04 7
2019-01-05 8
Select the first entry of each time series
>>> df.slice_by_timestep(0, 1)
target
item_id timestamp
0 2019-01-01 0
1 2019-01-02 3
2 2019-01-03 6
Select the last 2 entries of each time series
>>> df.slice_by_timestep(-2, None)
target
item_id timestamp
0 2019-01-02 1
2019-01-03 2
1 2019-01-03 4
2019-01-04 5
2 2019-01-04 7
2019-01-05 8
Select all except the last entry of each time series
>>> df.slice_by_timestep(None, -1)
target
item_id timestamp
0 2019-01-01 0
2019-01-02 1
1 2019-01-02 3
2019-01-03 4
2 2019-01-03 6
2019-01-04 7
Copy the entire dataframe
>>> df.slice_by_timestep(None, None)
target
item_id timestamp
0 2019-01-01 0
2019-01-02 1
2019-01-03 2
1 2019-01-02 3
2019-01-03 4
2019-01-04 5
2 2019-01-03 6
2019-01-04 7
2019-01-05 8
"""
if start_index is not None and not isinstance(start_index, int):
raise ValueError(f"start_index must be of type int or None (got {type(start_index)})")
if end_index is not None and not isinstance(end_index, int):
raise ValueError(f"end_index must be of type int or None (got {type(end_index)})")
time_step_slice = slice(start_index, end_index)
result = self.groupby(level=ITEMID, sort=False, as_index=False).nth(time_step_slice)
result.static_features = self.static_features
result._cached_freq = self._cached_freq
return result
[docs] def slice_by_time(self, start_time: pd.Timestamp, end_time: pd.Timestamp) -> TimeSeriesDataFrame:
"""Select a subsequence from each time series between start (inclusive) and end (exclusive) timestamps.
Parameters
----------
start_time: pd.Timestamp
Start time (inclusive) of the slice for each time series.
end_time: pd.Timestamp
End time (exclusive) of the slice for each time series.
Returns
-------
ts_df: TimeSeriesDataFrame
A new time series dataframe containing entries of the original time series between start and end timestamps.
"""
if end_time < start_time:
raise ValueError(f"end_time {end_time} is earlier than start_time {start_time}")
nanosecond_before_end_time = end_time - pd.Timedelta(nanoseconds=1)
return TimeSeriesDataFrame(
self.loc[(slice(None), slice(start_time, nanosecond_before_end_time)), :],
static_features=self.static_features,
)
[docs] @classmethod
def from_pickle(cls, filepath_or_buffer: Any) -> "TimeSeriesDataFrame":
"""Convenience method to read pickled time series data frames. If the read pickle
file refers to a plain pandas DataFrame, it will be cast to a TimeSeriesDataFrame.
Parameters
----------
filepath_or_buffer: Any
Filename provided as a string or an ``IOBuffer`` containing the pickled object.
Returns
-------
ts_df: TimeSeriesDataFrame
The pickled time series data frame.
"""
try:
data = pd.read_pickle(filepath_or_buffer)
return data if isinstance(data, cls) else cls(data)
except Exception as err: # noqa
raise IOError(f"Could not load pickled data set due to error: {str(err)}")
[docs] def get_reindexed_view(self, freq: str = "S") -> TimeSeriesDataFrame:
"""Returns a new TimeSeriesDataFrame object with the same underlying data and
static features as the current data frame, except the time index is replaced by
a new "dummy" time series index with the given frequency. This is useful when
suggesting AutoGluon-TimeSeries to "ignore" the time information, for example when
dealing with irregularly sampled time series or sequences (e.g., financial time
series).
Parameters
----------
freq: str
Frequency string of the new time series data index.
Returns
TimeSeriesDataFrame: the new view object with replaced index, but the same underlying
data. Note that the underlying data is not copied.
"""
df_view = self.iloc[:] # return a view without copying data
# build the surrogate index
indexes = []
for i in self.item_ids:
idx = pd.MultiIndex.from_product(
[(i,), pd.date_range(self.DUMMY_INDEX_START_TIME, periods=len(self.loc[i]), freq=freq)]
)
indexes.append(idx)
new_index = pd.MultiIndex.from_tuples(np.concatenate(indexes), names=[ITEMID, TIMESTAMP])
df_view.set_index(new_index, inplace=True)
df_view._cached_freq = freq
return df_view
[docs] def to_regular_index(self, freq: str) -> "TimeSeriesDataFrame":
"""Fill the gaps in an irregularly-sampled time series with NaNs.
Parameters
----------
freq: str
Frequency string of the new time series data index.
Examples
--------
>>> print(ts_dataframe)
target
item_id timestamp
0 2019-01-01 NaN
2019-01-03 1.0
2019-01-06 2.0
2019-01-07 NaN
1 2019-02-04 3.0
2019-02-07 4.0
>>> print(ts_dataframe.to_regular_index(freq="D"))
target
item_id timestamp
0 2019-01-01 NaN
2019-01-02 NaN
2019-01-03 1.0
2019-01-04 NaN
2019-01-05 NaN
2019-01-06 2.0
2019-01-07 NaN
1 2019-02-04 3.0
2019-02-05 NaN
2019-02-06 NaN
2019-02-07 4.0
"""
if self.freq is not None:
if self.freq != freq:
raise ValueError(
f"TimeSeriesDataFrame already has a regular index with freq '{self.freq}' "
f"that cannot be converted to the given freq '{freq}'"
)
else:
return self
filled_series = []
for item_id, time_series in self.groupby(level=ITEMID, sort=False):
time_series = time_series.droplevel(ITEMID)
timestamps = time_series.index
resampled_ts = time_series.resample(freq).asfreq()
if not timestamps.isin(resampled_ts.index).all():
raise ValueError(
f"Irregularly-sampled timestamps in this TimeSeriesDataFrame are not compatible "
f"with the given frequency '{freq}'"
)
filled_series.append(pd.concat({item_id: resampled_ts}, names=[ITEMID]))
return TimeSeriesDataFrame(pd.concat(filled_series), static_features=self.static_features)
[docs] def fill_missing_values(self, method: str = "auto", value: float = 0.0) -> "TimeSeriesDataFrame":
"""Fill missing values represented by NaN.
Parameters
----------
method : str, default = "auto"
Method used to impute missing values.
- "auto" - first forward fill (to fill the in-between and trailing NaNs), then backward fill (to fill the leading NaNs)
- "ffill" or "pad" - propagate last valid observation forward. Note: missing values at the start of the time series are not filled.
- "bfill" or "backfill" - use next valid observation to fill gap. Note: this may result in information leakage; missing values at the end of the time series are not filled.
- "constant" - replace NaNs with the given constant ``value``.
- "interpolate" - fill NaN values using linear interpolation. Note: this may result in information leakage.
value : float, default = 0.0
Value used by the "constant" imputation method.
Examples
--------
>>> print(ts_dataframe)
target
item_id timestamp
0 2019-01-01 NaN
2019-01-02 NaN
2019-01-03 1.0
2019-01-04 NaN
2019-01-05 NaN
2019-01-06 2.0
2019-01-07 NaN
1 2019-02-04 NaN
2019-02-05 3.0
2019-02-06 NaN
2019-02-07 4.0
>>> print(ts_dataframe.fill_missing_values(method="auto"))
target
item_id timestamp
0 2019-01-01 1.0
2019-01-02 1.0
2019-01-03 1.0
2019-01-04 1.0
2019-01-05 1.0
2019-01-06 2.0
2019-01-07 2.0
1 2019-02-04 3.0
2019-02-05 3.0
2019-02-06 3.0
2019-02-07 4.0
"""
if self.freq is None:
raise ValueError(
"Please make sure that all time series have a regular index before calling `fill_missing_values`"
"(for example, using the `to_regular_index` method)."
)
grouped_df = pd.DataFrame(self).groupby(level=ITEMID, sort=False, group_keys=False)
if method == "auto":
filled_df = grouped_df.fillna(method="ffill").fillna(method="bfill")
elif method in ["ffill", "pad"]:
filled_df = grouped_df.fillna(method="ffill")
elif method in ["bfill", "backfill"]:
filled_df = grouped_df.fillna(method="bfill")
elif method == "constant":
filled_df = self.fillna(value=value)
elif method == "interpolate":
filled_df = grouped_df.apply(lambda ts: ts.interpolate())
else:
raise ValueError(
"Invalid fill method. Expecting one of "
"{'auto', 'ffill', 'pad', 'bfill', 'backfill', 'constant', 'interpolate'}. "
f"Got {method}"
)
return TimeSeriesDataFrame(filled_df, static_features=self.static_features)
[docs] def dropna(self, how: str = "any") -> "TimeSeriesDataFrame":
"""Drop rows containing NaNs.
Parameters
----------
how : {"any", "all"}, default = "any"
Determine if row or column is removed from TimeSeriesDataFrame, when we have at least one NaN or all NaN.
- "any" : If any NaN values are present, drop that row or column.
- "all" : If all values are NaN, drop that row or column.
"""
# We need to cast to a DataFrame first. Calling self.dropna() results in an exception because self.T
# (used inside dropna) is not supported for TimeSeriesDataFrame
dropped_df = pd.DataFrame(self).dropna(how=how)
return TimeSeriesDataFrame(dropped_df, static_features=self.static_features)
[docs] def train_test_split(
self,
prediction_length: int,
end_index: Optional[int] = None,
suffix: Optional[str] = None,
) -> Tuple[TimeSeriesDataFrame, TimeSeriesDataFrame]:
"""Generate a train/test split from the given dataset.
This method can be used to generate splits for multi-window backtesting.
Parameters
----------
prediction_length : int
Number of time steps in a single evaluation window.
end_index : int, optional
If given, all time series will be shortened up to ``end_idx`` before the train/test splitting. In other
words, test data will include the slice ``[:end_index]`` of each time series, and train data will include
the slice ``[:end_index - prediction_length]``.
suffix : str, optional
Suffix appended to all entries in the ``item_id`` index level.
Returns
-------
train_data : TimeSeriesDataFrame
Train portion of the data. Contains the slice ``[:-prediction_length]`` of each time series in ``test_data``.
test_data : TimeSeriesDataFrame
Test portion of the data. Contains the slice ``[:end_idx]`` of each time series in the original dataset.
"""
test_data = self.slice_by_timestep(None, end_index)
train_data = test_data.slice_by_timestep(None, -prediction_length)
if suffix is not None:
for data in [train_data, test_data]:
new_item_id = data.index.levels[0].astype(str) + suffix
data.index = data.index.set_levels(levels=new_item_id, level=0)
if data.static_features is not None:
data.static_features.index = data.static_features.index.astype(str)
data.static_features.index += suffix
return train_data, test_data