def test_timeseries_constructor_error_log(self):
# test assert error log when trying to construct a TimeSeries that is too short
empty_series = pd.DataFrame()
with LogCapture() as lc:
get_logger("darts.timeseries").handlers = []
try:
TimeSeries(xr.DataArray(empty_series))
except Exception:
pass
lc.check((
"darts.timeseries",
"ERROR",
"ValueError: The time series array must not be empty.",
))
def test_raise_log(self):
exception_was_raised = False
with LogCapture() as lc:
logger = get_logger(__name__)
logger.handlers = []
try:
raise_log(Exception("test"), logger)
except Exception:
exception_was_raised = True
# testing correct log message
lc.check((__name__, "ERROR", "Exception: test"))
# checking whether exception was properly raised
self.assertTrue(exception_was_raised)
def test_time_log(self):
logger = get_logger(__name__)
logger.handlers = []
@time_log(logger)
def _my_timed_fn():
# do something for some time
for _ in range(2):
pass
with LogCapture() as lc:
_my_timed_fn()
logged_message = lc.records[-1].getMessage()
self.assertTrue(
re.match("_my_timed_fn function ran for [0-9]+ milliseconds",
logged_message))
def test_timeseries_split_error_log(self):
# test raised error log that occurs when trying to split TimeSeries at a point outside of the time index range
times = pd.date_range(start="2000-01-01", periods=3, freq="D")
values = np.array(range(3))
ts = TimeSeries.from_times_and_values(times, values)
with LogCapture() as lc:
get_logger("darts.timeseries").handlers = []
try:
ts.split_after(pd.Timestamp("2020-02-01"))
except Exception:
pass
lc.check((
"darts.timeseries",
"ERROR",
"ValueError: Timestamp must be between 2000-01-01 00:00:00 and 2000-01-03 00:00:00",
))
from typing import List, Optional
import numpy as np
import statsmodels.tsa.holtwinters as hw
from darts.logging import get_logger, raise_if_not, raise_log
from darts.models.forecasting.forecasting_model import ForecastingModel
from darts.timeseries import TimeSeries
from darts.utils.statistics import (
check_seasonality,
extract_trend_and_seasonality,
remove_from_series,
)
from darts.utils.utils import ModelMode, SeasonalityMode, TrendMode
logger = get_logger(__name__)
ALPHA_START = 0.2
class Theta(ForecastingModel):
# .. todo: Implement OTM: Optimized Theta Method (https://arxiv.org/pdf/1503.03529.pdf)
# .. todo: Try with something different than SES? They do that in the paper.
def __init__(
self,
theta: int = 2,
seasonality_period: Optional[int] = None,
season_mode: SeasonalityMode = SeasonalityMode.MULTIPLICATIVE,
):
"""
An implementation of the Theta method with configurable `theta` parameter. See [1]_.
