- 2.17.0 (latest)
- 2.16.0
- 2.15.0
- 2.14.0
- 2.13.0
- 2.12.0
- 2.11.0
- 2.10.0
- 2.9.0
- 2.8.0
- 2.7.0
- 2.6.0
- 2.5.0
- 2.4.0
- 2.3.0
- 2.2.0
- 2.0.0-dev0
- 1.36.0
- 1.35.0
- 1.34.0
- 1.33.0
- 1.32.0
- 1.31.0
- 1.30.0
- 1.29.0
- 1.28.0
- 1.27.0
- 1.26.0
- 1.25.0
- 1.24.0
- 1.22.0
- 1.21.0
- 1.20.0
- 1.19.0
- 1.18.0
- 1.17.0
- 1.16.0
- 1.15.0
- 1.14.0
- 1.13.0
- 1.12.0
- 1.11.1
- 1.10.0
- 1.9.0
- 1.8.0
- 1.7.0
- 1.6.0
- 1.5.0
- 1.4.0
- 1.3.0
- 1.2.0
- 1.1.0
- 1.0.0
- 0.26.0
- 0.25.0
- 0.24.0
- 0.23.0
- 0.22.0
- 0.21.0
- 0.20.1
- 0.19.2
- 0.18.0
- 0.17.0
- 0.16.0
- 0.15.0
- 0.14.1
- 0.13.0
- 0.12.0
- 0.11.0
- 0.10.0
- 0.9.0
- 0.8.0
- 0.7.0
- 0.6.0
- 0.5.0
- 0.4.0
- 0.3.0
- 0.2.0
Forcasting models.
Classes
ARIMAPlus
ARIMAPlus
(
*
,
horizon
:
int
=
1000
,
auto_arima
:
bool
=
True
,
auto_arima_max_order
:
typing
.
Optional
[
int
]
=
None
,
auto_arima_min_order
:
typing
.
Optional
[
int
]
=
None
,
data_frequency
:
str
=
"auto_frequency"
,
include_drift
:
bool
=
False
,
holiday_region
:
typing
.
Optional
[
str
]
=
None
,
clean_spikes_and_dips
:
bool
=
True
,
adjust_step_changes
:
bool
=
True
,
time_series_length_fraction
:
typing
.
Optional
[
float
]
=
None
,
min_time_series_length
:
typing
.
Optional
[
int
]
=
None
,
max_time_series_length
:
typing
.
Optional
[
int
]
=
None
,
trend_smoothing_window_size
:
typing
.
Optional
[
int
]
=
None
,
decompose_time_series
:
bool
=
True
)
Time Series ARIMA Plus model.
horizon
int, default 1,000
The number of time points to forecast. Default to 1,000, max value 10,000.
auto_arima
bool, default True
Determines whether the training process uses auto.ARIMA or not. If True, training automatically finds the best non-seasonal order (that is, the p, d, q tuple) and decides whether or not to include a linear drift term when d is 1.
auto_arima_max_order
int or None, default None
The maximum value for the sum of non-seasonal p and q.
auto_arima_min_order
int or None, default None
The minimum value for the sum of non-seasonal p and q.
data_frequency
str, default "auto_frequency"
The data frequency of the input time series. Possible values are "auto_frequency", "per_minute", "hourly", "daily", "weekly", "monthly", "quarterly", "yearly"
include_drift
bool, default False
Determines whether the model should include a linear drift term or not. The drift term is applicable when non-seasonal d is 1.
holiday_region
str or None, default None
The geographical region based on which the holiday effect is applied in modeling. By default, holiday effect modeling isn't used. Possible values see https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-time-series#holiday_region .
clean_spikes_and_dips
bool, default True
Determines whether or not to perform automatic spikes and dips detection and cleanup in the model training pipeline. The spikes and dips are replaced with local linear interpolated values when they're detected.
adjust_step_changes
bool, default True
Determines whether or not to perform automatic step change detection and adjustment in the model training pipeline.
time_series_length_fraction
float or None, default None
The fraction of the interpolated length of the time series that's used to model the time series trend component. All of the time points of the time series are used to model the non-trend component.
min_time_series_length
int or None, default None
The minimum number of time points that are used in modeling the trend component of the time series.
max_time_series_length
int or None, default None
The maximum number of time points in a time series that can be used in modeling the trend component of the time series.
trend_smoothing_window_size
int or None, default None
The smoothing window size for the trend component.
decompose_time_series
bool, default True
Determines whether the separate components of both the history and forecast parts of the time series (such as holiday effect and seasonal components) are saved in the model.
