merlion.models.forecast package
Contains all forecasting models.
For forecasting, we define an abstract base ForecasterBase
class which inherits from ModelBase
and supports the
following interface, in addition to model.save()
and ForecasterClass.load
defined for ModelBase
:
model = ForecasterClass(config)
initialization with a model-specific config (which inherits from
ForecasterConfig
)configs contain:
a (potentially trainable) data pre-processing transform from
merlion.transform
; note thatmodel.transform
is a property which refers tomodel.config.transform
model-specific hyperparameters
optionally, a maximum number of steps the model can forecast for
model.forecast(time_stamps, time_series_prev=None)
returns the forecast (
TimeSeries
) for future values at the time stamps specified bytime_stamps
, as well as the standard error of that forecast (TimeSeries
, may beNone
)if
time_series_prev
is specified, it is used as the most recent context. Otherwise, the training data is used
model.train(train_data, train_config=None)
trains the model on the
TimeSeries
train_data
train_config
(optional): extra configuration describing how the model should be trained (e.g. learning rate forLSTM
). Not used for all models. Class-level default provided for models which do use it.returns the model’s prediction
train_data
, in the same format as if you calledForecasterBase.forecast
on the time stamps oftrain_data
Base class for forecasting models. |
|
The classic statistical forecasting model ARIMA (AutoRegressive Integrated Moving Average). |
|
A variant of ARIMA with a user-specified Seasonality. |
|
Wrapper around Facebook's popular Prophet model for time series forecasting. |
|
Multi-Scale Exponential Smoother for univariate time series forecasting. |
|
Vector AutoRegressive model for multivariate time series forecasting. |
|
Bagging Tree-based models for multivariate time series forecasting. |
|
Boosting Tree-based models for multivariate time series forecasting. |
|
A forecaster based on a LSTM neural net. |
Submodules
merlion.models.forecast.base module
Base class for forecasting models.
- class merlion.models.forecast.base.ForecasterConfig(max_forecast_steps, target_seq_index=None, **kwargs)
Bases:
Config
Config object used to define a forecaster model.
- Parameters
max_forecast_steps (
Optional
[int
]) – Max # of steps we would like to forecast for. Required for some models which pre-compute a forecast, like ARIMA, SARIMA, and LSTM.target_seq_index (
Optional
[int
]) – The index of the univariate (amongst all univariates in a general multivariate time series) whose value we would like to forecast.
- classmethod from_dict(config_dict, return_unused_kwargs=False, **kwargs)
Constructs a
Config
from a Python dictionary of parameters.- Parameters
config_dict (
Dict
[str
,Any
]) – dict that will be used to instantiate this object.return_unused_kwargs – whether to return any unused keyword args.
kwargs – any additional parameters to set (overriding config_dict).
- Returns
Config
object initialized from the dict.
- class merlion.models.forecast.base.ForecasterBase(config)
Bases:
ModelBase
Base class for a forecaster model.
Note
If your model depends on an evenly spaced time series, make sure to
Call
ForecasterBase.train_pre_process
inForecasterBase.train
Call
ForecasterBase.resample_time_stamps
at the start ofForecasterBase.forecast
to get a set of resampled time stamps, and calltime_series.align(reference=time_stamps)
to align the forecast with the original time stamps.
- config_class
alias of
ForecasterConfig
- property max_forecast_steps
- property target_seq_index: int
- Return type
int
- Returns
the index of the univariate (amongst all univariates in a general multivariate time series) whose value we would like to forecast.
- property dim
- resample_time_stamps(time_stamps, time_series_prev=None)
- train_pre_process(train_data, require_even_sampling, require_univariate)
Applies pre-processing steps common for training most models.
- Parameters
train_data (
TimeSeries
) – the original time series of training datarequire_even_sampling (
bool
) – whether the model assumes that training data is sampled at a fixed frequencyrequire_univariate (
bool
) – whether the model only works with univariate time series
- Return type
- Returns
the training data, after any necessary pre-processing has been applied
- abstract train(train_data, train_config=None)
Trains the forecaster on the input time series.
- Parameters
train_data (
TimeSeries
) – aTimeSeries
of metric values to train the model.train_config – Additional training configs, if needed. Only required for some models.
- Return type
Tuple
[TimeSeries
,Optional
[TimeSeries
]]- Returns
the model’s prediction on
train_data
, in the same format as if you calledForecasterBase.forecast
on the time stamps oftrain_data
- abstract forecast(time_stamps, time_series_prev=None, return_iqr=False, return_prev=False)
Returns the model’s forecast on the timestamps given. Note that if
self.transform
is specified in the config, the forecast is a forecast of transformed values! It is up to you to manually invert the transform if desired.- Parameters
time_stamps (
Union
[int
,List
[int
]]) – Either alist
of timestamps we wish to forecast for, or the number of steps (int
) we wish to forecast for.time_series_prev (
Optional
[TimeSeries
]) – a list of (timestamp, value) pairs immediately precedingtime_series
. If given, we use it to initialize the time series model. Otherwise, we assume thattime_series
immediately follows the training data.return_iqr (
bool
) – whether to return the inter-quartile range for the forecast. Note that not all models support this option.return_prev (
bool
) – whether to return the forecast fortime_series_prev
(and its stderr or IQR if relevant), in addition to the forecast fortime_stamps
. Only used iftime_series_prev
is provided.
- Return type
Union
[Tuple
[TimeSeries
,Optional
[TimeSeries
]],Tuple
[TimeSeries
,TimeSeries
,TimeSeries
]]- Returns
(forecast, forecast_stderr)
ifreturn_iqr
is false,(forecast, forecast_lb, forecast_ub)
otherwise.forecast
: the forecast for the timestamps givenforecast_stderr
: the standard error of each forecast value.May be
None
.
forecast_lb
: 25th percentile of forecast values for each timestampforecast_ub
: 75th percentile of forecast values for each timestamp
- batch_forecast(time_stamps_list, time_series_prev_list, return_iqr=False, return_prev=False)
Returns the model’s forecast on a batch of timestamps given. Note that if
self.transform
is specified in the config, the forecast is a forecast of transformed values! It is up to you to manually invert the transform if desired.- Parameters
time_stamps_list (
List
[List
[int
]]) – a list of lists of timestamps we wish to forecast fortime_series_prev_list (
List
[TimeSeries
]) – a list of TimeSeries immediately preceding the time stamps in time_stamps_listreturn_iqr (
bool
) – whether to return the inter-quartile range for the forecast. Note that not all models support this option.return_prev (
bool
) – whether to return the forecast fortime_series_prev
(and its stderr or IQR if relevant), in addition to the forecast fortime_stamps
. Only used iftime_series_prev
is provided.
- Return type
Tuple
[Union
[Tuple
[List
[TimeSeries
],List
[Optional
[TimeSeries
]]],Tuple
[List
[TimeSeries
],List
[TimeSeries
],List
[TimeSeries
]]]]- Returns
(forecast, forecast_stderr)
ifreturn_iqr
is false,(forecast, forecast_lb, forecast_ub)
otherwise.forecast
: the forecast for the timestamps givenforecast_stderr
: the standard error of each forecast value.May be
None
.
forecast_lb
: 25th percentile of forecast values for each timestampforecast_ub
: 75th percentile of forecast values for each timestamp
- invert_transform(forecast, time_series_prev=None)
- get_figure(*, time_series=None, time_stamps=None, time_series_prev=None, plot_forecast_uncertainty=False, plot_time_series_prev=False)
- Parameters
time_series (
Optional
[TimeSeries
]) – the time series over whose timestamps we wish to make a forecast. Exactly one oftime_series
ortime_stamps
should be provided.time_stamps (
Optional
[List
[int
]]) – a list of timestamps we wish to forecast for. Exactly one oftime_series
ortime_stamps
should be provided.time_series_prev (
Optional
[TimeSeries
]) – aTimeSeries
immediately precedingtime_stamps
. If given, we use it to initialize the time series model. Otherwise, we assume thattime_stamps
immediately follows the training data.plot_forecast_uncertainty – whether to plot uncertainty estimates (the inter-quartile range) for forecast values. Not supported for all models.
plot_time_series_prev – whether to plot
time_series_prev
(and the model’s fit for it). Only used iftime_series_prev
is given.
- Return type
- Returns
a
Figure
of the model’s forecast.
- plot_forecast(*, time_series=None, time_stamps=None, time_series_prev=None, plot_forecast_uncertainty=False, plot_time_series_prev=False, figsize=(1000, 600), ax=None)
Plots the forecast for the time series in matplotlib, optionally also plotting the uncertainty of the forecast, as well as the past values (both true and predicted) of the time series.
- Parameters
time_series (
Optional
[TimeSeries
]) – the time series over whose timestamps we wish to make a forecast. Exactly one oftime_series
ortime_stamps
should be provided.time_stamps (
Optional
[List
[int
]]) – a list of timestamps we wish to forecast for. Exactly one oftime_series
ortime_stamps
should be provided.time_series_prev (
Optional
[TimeSeries
]) – aTimeSeries
immediately precedingtime_stamps
. If given, we use it to initialize the time series model. Otherwise, we assume thattime_stamps
immediately follows the training data.plot_forecast_uncertainty – whether to plot uncertainty estimates (the inter-quartile range) for forecast values. Not supported for all models.
plot_time_series_prev – whether to plot
time_series_prev
(and the model’s fit for it). Only used iftime_series_prev
is given.figsize – figure size in pixels
ax – matplotlib axis to add this plot to
- Returns
(fig, ax): matplotlib figure & axes the figure was plotted on
- plot_forecast_plotly(*, time_series=None, time_stamps=None, time_series_prev=None, plot_forecast_uncertainty=False, plot_time_series_prev=False, figsize=(1000, 600))
Plots the forecast for the time series in plotly, optionally also plotting the uncertainty of the forecast, as well as the past values (both true and predicted) of the time series.
- Parameters
time_series (
Optional
[TimeSeries
]) – the time series over whose timestamps we wish to make a forecast. Exactly one oftime_series
ortime_stamps
should be provided.time_stamps (
Optional
[List
[int
]]) – a list of timestamps we wish to forecast for. Exactly one oftime_series
ortime_stamps
should be provided.time_series_prev (
Optional
[TimeSeries
]) – aTimeSeries
immediately precedingtime_stamps
. If given, we use it to initialize the time series model. Otherwise, we assume thattime_stamps
immediately follows the training data.plot_forecast_uncertainty – whether to plot uncertainty estimates (the inter-quartile range) for forecast values. Not supported for all models.
plot_time_series_prev – whether to plot
time_series_prev
(and the model’s fit for it). Only used iftime_series_prev
is given.figsize – figure size in pixels
merlion.models.forecast.arima module
The classic statistical forecasting model ARIMA (AutoRegressive Integrated Moving Average).
- class merlion.models.forecast.arima.ArimaConfig(max_forecast_steps=None, target_seq_index=None, order=(4, 1, 2), **kwargs)
Bases:
SarimaConfig
Config object used to define a forecaster model.
Configuration class for Arima. Just a Sarima model with seasonal order (0, 0, 0, 0).
- property seasonal_order: Tuple[int, int, int, int]
- Return type
Tuple
[int
,int
,int
,int
]- Returns
(0, 0, 0, 0) because ARIMA has no seasonal order.
- class merlion.models.forecast.arima.Arima(config)
Bases:
Sarima
Implementation of the classic statistical model ARIMA (AutoRegressive Integrated Moving Average) for forecasting.
- config_class
alias of
ArimaConfig
merlion.models.forecast.sarima module
A variant of ARIMA with a user-specified Seasonality.
- class merlion.models.forecast.sarima.SarimaConfig(max_forecast_steps=None, target_seq_index=None, order=(4, 1, 2), seasonal_order=(2, 0, 1, 24), **kwargs)
Bases:
ForecasterConfig
Config object used to define a forecaster model.
Configuration class for Sarima.
- Parameters
max_forecast_steps – Number of steps we would like to forecast for.
target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time series) whose value we would like to forecast.
order – Order is (p, d, q) for an ARIMA(p, d, q) process. d must be an integer indicating the integration order of the process, while p and q must be integers indicating the AR and MA orders (so that all lags up to those orders are included).
seasonal_order – Seasonal order is (P, D, Q, S) for seasonal ARIMA process, where s is the length of the seasonality cycle (e.g. s=24 for 24 hours on hourly granularity). P, D, Q are as for ARIMA.
- class merlion.models.forecast.sarima.Sarima(config)
Bases:
ForecasterBase
,SeasonalityModel
Implementation of the classic statistical model SARIMA (Seasonal AutoRegressive Integrated Moving Average) for forecasting.
- config_class
alias of
SarimaConfig
- property order: Tuple[int, int, int]
- Return type
Tuple
[int
,int
,int
]- Returns
the order (p, d, q) of the model, where p is the AR order, d is the integration order, and q is the MA order.
- property seasonal_order: Tuple[int, int, int, int]
- Return type
Tuple
[int
,int
,int
,int
]- Returns
the seasonal order (P, D, Q, S) for the seasonal ARIMA process, where p is the AR order, D is the integration order, Q is the MA order, and S is the length of the seasonality cycle.
- train(train_data, train_config=None)
Trains the forecaster on the input time series.
- Parameters
train_data (
TimeSeries
) – aTimeSeries
of metric values to train the model.train_config – Additional training configs, if needed. Only required for some models.
- Returns
the model’s prediction on
train_data
, in the same format as if you calledForecasterBase.forecast
on the time stamps oftrain_data
- forecast(time_stamps, time_series_prev=None, return_iqr=False, return_prev=False)
Returns the model’s forecast on the timestamps given. Note that if
self.transform
is specified in the config, the forecast is a forecast of transformed values! It is up to you to manually invert the transform if desired.- Parameters
time_stamps (
Union
[int
,List
[int
]]) – Either alist
of timestamps we wish to forecast for, or the number of steps (int
) we wish to forecast for.time_series_prev (
Optional
[TimeSeries
]) – a list of (timestamp, value) pairs immediately precedingtime_series
. If given, we use it to initialize the time series model. Otherwise, we assume thattime_series
immediately follows the training data.return_iqr – whether to return the inter-quartile range for the forecast. Note that not all models support this option.
return_prev – whether to return the forecast for
time_series_prev
(and its stderr or IQR if relevant), in addition to the forecast fortime_stamps
. Only used iftime_series_prev
is provided.
- Return type
Union
[Tuple
[TimeSeries
,TimeSeries
],Tuple
[TimeSeries
,TimeSeries
,TimeSeries
]]- Returns
(forecast, forecast_stderr)
ifreturn_iqr
is false,(forecast, forecast_lb, forecast_ub)
otherwise.forecast
: the forecast for the timestamps givenforecast_stderr
: the standard error of each forecast value.May be
None
.
forecast_lb
: 25th percentile of forecast values for each timestampforecast_ub
: 75th percentile of forecast values for each timestamp
- set_seasonality(theta, train_data)
Implement this method to do any model-specific adjustments on the seasonality that was provided by
SeasonalityLayer
.- Parameters
theta – Seasonality processed by
SeasonalityLayer
.train_data (
array
) – Training data (or numpy array representing the target univariate) for any model-specific adjustments you might want to make.
merlion.models.forecast.prophet module
Wrapper around Facebook’s popular Prophet model for time series forecasting.
- class merlion.models.forecast.prophet.ProphetConfig(max_forecast_steps=None, target_seq_index=None, yearly_seasonality='auto', weekly_seasonality='auto', daily_seasonality='auto', add_seasonality='auto', uncertainty_samples=100, **kwargs)
Bases:
ForecasterConfig
Config object used to define a forecaster model.
Configuration class for Facebook’s Prophet model, as described in this paper.
- Parameters
max_forecast_steps (
Optional
[int
]) – Max # of steps we would like to forecast for.target_seq_index (
Optional
[int
]) – The index of the univariate (amongst all univariates in a general multivariate time series) whose value we would like to forecast.yearly_seasonality (
Union
[bool
,int
]) – If bool, whether to enable yearly seasonality. By default, it is activated if there are >= 2 years of history, but deactivated otherwise. If int, this is the number of Fourier series components used to model the seasonality (default = 10).weekly_seasonality (
Union
[bool
,int
]) – If bool, whether to enable weekly seasonality. By default, it is activated if there are >= 2 weeks of history, but deactivated otherwise. If int, this is the number of Fourier series components used to model the seasonality (default = 3).daily_seasonality (
Union
[bool
,int
]) – If bool, whether to enable daily seasonality. By default, it is activated if there are >= 2 days of history, but deactivated otherwise. If int, this is the number of Fourier series components used to model the seasonality (default = 4).add_seasonality – ‘auto’ indicates automatically adding extra seasonaltiy by detection methods (default = None).
uncertainty_samples (
int
) – The number of posterior samples to draw in order to calibrate the anomaly scores.
- class merlion.models.forecast.prophet.Prophet(config)
Bases:
ForecasterBase
Facebook’s model for time series forecasting. See docs for
ProphetConfig
and the paper for more details.- config_class
alias of
ProphetConfig
- property yearly_seasonality
- property weekly_seasonality
- property daily_seasonality
- property add_seasonality
- property uncertainty_samples
- train(train_data, train_config=None)
Trains the forecaster on the input time series.
- Parameters
train_data (
TimeSeries
) – aTimeSeries
of metric values to train the model.train_config – Additional training configs, if needed. Only required for some models.
- Returns
the model’s prediction on
train_data
, in the same format as if you calledForecasterBase.forecast
on the time stamps oftrain_data
- forecast(time_stamps, time_series_prev=None, return_iqr=False, return_prev=False)
Returns the model’s forecast on the timestamps given. Note that if
self.transform
is specified in the config, the forecast is a forecast of transformed values! It is up to you to manually invert the transform if desired.- Parameters
time_stamps (
Union
[int
,List
[int
]]) – Either alist
of timestamps we wish to forecast for, or the number of steps (int
) we wish to forecast for.time_series_prev (
Optional
[TimeSeries
]) – a list of (timestamp, value) pairs immediately precedingtime_series
. If given, we use it to initialize the time series model. Otherwise, we assume thattime_series
immediately follows the training data.return_iqr – whether to return the inter-quartile range for the forecast. Note that not all models support this option.
return_prev – whether to return the forecast for
time_series_prev
(and its stderr or IQR if relevant), in addition to the forecast fortime_stamps
. Only used iftime_series_prev
is provided.
- Return type
Union
[Tuple
[TimeSeries
,TimeSeries
],Tuple
[TimeSeries
,TimeSeries
,TimeSeries
]]- Returns
(forecast, forecast_stderr)
ifreturn_iqr
is false,(forecast, forecast_lb, forecast_ub)
otherwise.forecast
: the forecast for the timestamps givenforecast_stderr
: the standard error of each forecast value.May be
None
.
forecast_lb
: 25th percentile of forecast values for each timestampforecast_ub
: 75th percentile of forecast values for each timestamp
merlion.models.forecast.smoother module
Multi-Scale Exponential Smoother for univariate time series forecasting.
- class merlion.models.forecast.smoother.MSESConfig(max_forecast_steps, target_seq_index=None, max_backstep=None, recency_weight=0.5, accel_weight=1.0, optimize_acc=True, eta=0.0, rho=0.0, phi=2.0, inflation=1.0, **kwargs)
Bases:
ForecasterConfig
Configuration class for an MSES forecasting model.
Letting
w
be the recency weight,B
the maximum backstep,x_t
the last seen data point, andl_s,t
the series of losses for scales
.\[\begin{split}\begin{align*} \hat{x}_{t+h} & = \sum_{b=0}^B p_{b} \cdot (x_{t-b} + v_{b+h,t} + a_{b+h,t}) \\ \space \\ \text{where} \space\space & v_{b+h,t} = \text{EMA}_w(\Delta_{b+h} x_t) \\ & a_{b+h,t} = \text{EMA}_w(\Delta_{b+h}^2 x_t) \\ \text{and} \space\space & p_b = \sigma(z)_b \space\space \\ \text{if} & \space\space z_b = (b+h)^\phi \cdot \text{EMA}_w(l_{b+h,t}) \cdot \text{RWSE}_w(l_{b+h,t})\\ \end{align*}\end{split}\]- Parameters
max_forecast_steps (
int
) – Max number of steps to forecast ahead.target_seq_index (
Optional
[int
]) – The index of the univariate (amongst all univariates in a general multivariate time series) whose value we would like to forecast.max_backstep (
Optional
[int
]) – Max backstep to use in forecasting. If we train with x(0),…,x(t), Then, the b-th model MSES uses will forecast x(t+h) by anchoring at x(t-b) and predicting xhat(t+h) = x(t-b) + delta_hat(b+h).recency_weight (
float
) – The recency weight parameter to use when estimating delta_hat.accel_weight (
float
) – The weight to scale the acceleration by when computing delta_hat. Specifically, delta_hat(b+h) = velocity(b+h) + accel_weight * acceleration(b+h).optimize_acc (
bool
) – If True, the acceleration correction will only be used at scales ranging from 1,…(max_backstep+max_forecast_steps)/2.eta (
float
) – The parameter used to control the rate at which recency_weight gets tuned when online updates are made to the model and losses can be computed.rho (
float
) – The parameter that determines what fraction of the overall error is due to velcity error, while the rest is due to the complement. The error at any scale will be determined asrho * velocity_error + (1-rho) * loss_error
.phi (
float
) – The parameter used to exponentially inflate the magnitude of loss error at different scales. Loss error for scales
will be increased by a factor ofphi ** s
.inflation (
float
) – The inflation exponent to use when computing the distribution p(b|h) over the models when forecasting at horizon h according to standard errors of the estimated velocities over the models; inflation=1 is equivalent to using the softmax function.
- property max_scale
- property backsteps
- class merlion.models.forecast.smoother.MSESTrainConfig(incremental=True, process_losses=True, tune_recency_weights=False, init_batch_sz=2, train_cadence=None)
Bases:
object
MSES training configuration.
- Parameters
incremental (
bool
) – If True, train the MSES model incrementally with the initial training data at the giventrain_cadence
. This allows MSES to return a forecast for the training data.init_batch_sz (
int
) – The size of the inital training batch for MSES. This is necessary because MSES cannot predict the past, but needs to start with some data. This should be very small. 2 is the minimum, and is recommended because 2 will result in the most representative train forecast.train_cadence (
Optional
[int
]) – The frequency at which the training forecasts will be generated during incremental training.
- Param
If True, track the losses encountered during incremental initial training.
- Tune_recency_weights
If True, tune recency weights during incremental initial training.
- class merlion.models.forecast.smoother.MSES(config)
Bases:
ForecasterBase
Multi-scale Exponential Smoother (MSES) is a forecasting algorithm modeled heavily after classical mechanical concepts, namely, velocity and acceleration.
Having seen data points of a time series up to time t, MSES forecasts x(t+h) by anchoring at a value b steps back from the last known value, x(t-b), and estimating the delta between x(t-b) and x(t+h). The delta over these b+h timesteps, delta(b+h), also known as the delta at scale b+h, is predicted by estimating the velocity over these timesteps as well as the change in the velocity, acceleration. Specifically,
xhat(t+h) = x(t-b) + velocity_hat(b+h) + acceleration_hat(b+h)
This estimation is done for each b, known as a backstep, from 0, which anchors at x(t), 1,… up to a maximum backstep configurable by the user. The algorithm then takes the seperate forecasts of x(t+h), indexed by which backstep was used, xhat_b(t+h), and determines a final forecast: p(b|h) dot xhat_b, where p(b|h) is a distribution over the xhat_b’s that is determined according to the lowest standard errors of the recency-weighted velocity estimates.
Letting
w
be the recency weight,B
the maximum backstep,x_t
the last seen data point, andl_s,t
the series of losses for scales
.\[\begin{split}\begin{align*} \hat{x}_{t+h} & = \sum_{b=0}^B p_{b} \cdot (x_{t-b} + v_{b+h,t} + a_{b+h,t}) \\ \space \\ \text{where} \space\space & v_{b+h,t} = \text{EMA}_w(\Delta_{b+h} x_t) \\ & a_{b+h,t} = \text{EMA}_w(\Delta_{b+h}^2 x_t) \\ \text{and} \space\space & p_b = \sigma(z)_b \space\space \\ \text{if} & \space\space z_b = (b+h)^\phi \cdot \text{EMA}_w(l_{b+h,t}) \cdot \text{RWSE}_w(l_{b+h,t})\\ \end{align*}\end{split}\]- config_class
alias of
MSESConfig
- property rho
- property backsteps
- property max_horizon
- train(train_data, train_config=None)
Trains the forecaster on the input time series.
- Parameters
train_data (
TimeSeries
) – aTimeSeries
of metric values to train the model.train_config (
Optional
[MSESTrainConfig
]) – Additional training configs, if needed. Only required for some models.
- Return type
Tuple
[Optional
[TimeSeries
],None
]- Returns
the model’s prediction on
train_data
, in the same format as if you calledForecasterBase.forecast
on the time stamps oftrain_data
- update(new_data, tune_recency_weights=True, train_cadence=None)
Updates the MSES model with new data that has been acquired since the model’s initial training.
- Parameters
new_data (
TimeSeries
) – New data that has occured since the last training time.tune_recency_weights (
bool
) – If True, the model will first forecast the values at the new_data’s timestamps, calculate the associated losses, and use these losses to make updates to the recency weight.train_cadence – The frequency at which the training forecasts will be generated during incremental training.
- Return type
Tuple
[TimeSeries
,TimeSeries
]
- forecast(time_stamps, time_series_prev=None, return_iqr=False, return_prev=False)
Returns the model’s forecast on the timestamps given. Note that if
self.transform
is specified in the config, the forecast is a forecast of transformed values! It is up to you to manually invert the transform if desired.- Parameters
time_stamps (
Union
[int
,List
[int
]]) – Either alist
of timestamps we wish to forecast for, or the number of steps (int
) we wish to forecast for.time_series_prev (
Optional
[TimeSeries
]) – a list of (timestamp, value) pairs immediately precedingtime_series
. If given, we use it to initialize the time series model. Otherwise, we assume thattime_series
immediately follows the training data.return_iqr (
bool
) – whether to return the inter-quartile range for the forecast. Note that not all models support this option.return_prev (
bool
) – whether to return the forecast fortime_series_prev
(and its stderr or IQR if relevant), in addition to the forecast fortime_stamps
. Only used iftime_series_prev
is provided.
- Return type
Tuple
[TimeSeries
,None
]- Returns
(forecast, forecast_stderr)
ifreturn_iqr
is false,(forecast, forecast_lb, forecast_ub)
otherwise.forecast
: the forecast for the timestamps givenforecast_stderr
: the standard error of each forecast value.May be
None
.
forecast_lb
: 25th percentile of forecast values for each timestampforecast_ub
: 75th percentile of forecast values for each timestamp
- xhat_h(horizon)
Returns the forecasts for the input horizon at every backstep.
- Return type
List
[Optional
[float
]]
- marginalize_xhat_h(horizon, xhat_h)
Given a list of forecasted values produced by delta estimators at different backsteps, compute a weighted average of these values. The weights are assigned based on the standard errors of the velocities, where the b’th estimate will be given more weight if its velocity has a lower standard error relative to the other estimates.
- Parameters
horizon (
int
) – the horizon at which we want to predictxhat_h (
List
[Optional
[float
]]) – the forecasted values at this horizon, using each of the possible backsteps
- class merlion.models.forecast.smoother.DeltaStats(scale, recency_weight)
Bases:
object
A wrapper around the statistics used to estimate deltas at a given scale.
- Parameters
scale (
int
) – The scale associated with the statisticsrecency_weight (
float
) – The recency weight parameter that that the incremental velocity, acceleration and standard error statistics should use.
- property lag
- update_velocity(vels)
- update_acceleration(accs)
- update_loss(losses)
- tune(losses, eta)
Tunes the recency weight according to recent forecast losses.
- Parameters
losses (
List
[float
]) – List of recent losses.eta (
float
) – Constant by which to scale the update to the recency weight. A bigger eta means more aggressive updates to the recency_weight.
- class merlion.models.forecast.smoother.DeltaEstimator(max_scale, recency_weight, accel_weight, optimize_acc, eta, phi, data=None, stats=None)
Bases:
object
Class for estimating the delta for MSES.
- Parameters
max_scale (
int
) – Delta Estimator can estimate delta over multiple scales, or time steps, ranging from 1,2,…,max_scale.recency_weight (
float
) – The recency weight parameter to use when estimating delta_hat.accel_weight (
float
) – The weight to scale the acceleration by when computing delta_hat. Specifically, delta_hat(b+h) = velocity(b+h) + accel_weight * acceleration(b+h).optimize_acc (
bool
) – If True, the acceleration correction will only be used at scales ranging from 1,…,max_scale/2.eta (
float
) – The parameter used to control the rate at which recency_weight gets tuned when online updates are made to the model and losses can be computed.data (
Optional
[UnivariateTimeSeries
]) – The data to initialize the delta estimator with.stats (
Optional
[Dict
[int
,DeltaStats
]]) – Dictionary mapping scales to DeltaStats objects to be used for delta estimation.
- property acc_max_scale
- property max_scale
- property data
- property x
- train(new_data)
Updates the delta statistics: velocity, acceleration and velocity standard error at each scale using new data.
- Parameters
new_data (
UnivariateTimeSeries
) – new datapoints in the time series.
- process_losses(scale_losses, tune_recency_weights=False)
Uses recent forecast errors to improve the delta estimator. This is done by updating the recency_weight that is used by delta stats at particular scales.
- Parameters
scale_losses (
Dict
[int
,List
[float
]]) – A dictionary mapping a scale to a list of forecasting errors that associated with that scale.
- velocity(scale)
- Return type
float
- acceleration(scale)
- Return type
float
- vel_err(scale)
- Return type
float
- pos_err(scale)
- Return type
float
- neg_err(scale)
- Return type
float
- loss_err(scale)
- Return type
float
- delta_hat(scale)
- Return type
float
merlion.models.forecast.vector_ar module
Vector AutoRegressive model for multivariate time series forecasting.
- class merlion.models.forecast.vector_ar.VectorARConfig(max_forecast_steps, maxlags, target_seq_index=None, **kwargs)
Bases:
ForecasterConfig
Config object used to define a forecaster model.
- Parameters
max_forecast_steps (
int
) – Max # of steps we would like to forecast for.maxlags (
int
) – Max # of lags for ARtarget_seq_index (
Optional
[int
]) – The index of the univariate (amongst all univariates in a general multivariate time series) whose value we would like to forecast.maxlags – Max # of lags for AR
- class merlion.models.forecast.vector_ar.VectorAR(config)
Bases:
ForecasterBase
Vector AutoRegressive model for multivariate time series forecasting.
- config_class
alias of
VectorARConfig
- property maxlags: int
- Return type
int
- train(train_data, train_config=None)
Trains the forecaster on the input time series.
- Parameters
train_data (
TimeSeries
) – aTimeSeries
of metric values to train the model.train_config – Additional training configs, if needed. Only required for some models.
- Return type
Tuple
[TimeSeries
,TimeSeries
]- Returns
the model’s prediction on
train_data
, in the same format as if you calledForecasterBase.forecast
on the time stamps oftrain_data
- forecast(time_stamps, time_series_prev=None, return_iqr=False, return_prev=False)
Returns the model’s forecast on the timestamps given. Note that if
self.transform
is specified in the config, the forecast is a forecast of transformed values! It is up to you to manually invert the transform if desired.- Parameters
time_stamps (
Union
[int
,List
[int
]]) – Either alist
of timestamps we wish to forecast for, or the number of steps (int
) we wish to forecast for.time_series_prev (
Optional
[TimeSeries
]) – a list of (timestamp, value) pairs immediately precedingtime_series
. If given, we use it to initialize the time series model. Otherwise, we assume thattime_series
immediately follows the training data.return_iqr – whether to return the inter-quartile range for the forecast. Note that not all models support this option.
return_prev – whether to return the forecast for
time_series_prev
(and its stderr or IQR if relevant), in addition to the forecast fortime_stamps
. Only used iftime_series_prev
is provided.
- Return type
Union
[Tuple
[TimeSeries
,TimeSeries
],Tuple
[TimeSeries
,TimeSeries
,TimeSeries
]]- Returns
(forecast, forecast_stderr)
ifreturn_iqr
is false,(forecast, forecast_lb, forecast_ub)
otherwise.forecast
: the forecast for the timestamps givenforecast_stderr
: the standard error of each forecast value.May be
None
.
forecast_lb
: 25th percentile of forecast values for each timestampforecast_ub
: 75th percentile of forecast values for each timestamp
- set_data_already_transformed()
- reset_data_already_transformed()
merlion.models.forecast.baggingtrees module
Bagging Tree-based models for multivariate time series forecasting. Random Forest ExtraTreesRegressor
- class merlion.models.forecast.baggingtrees.BaggingTreeForecasterConfig(max_forecast_steps, maxlags, target_seq_index=None, sampling_mode='normal', prediction_stride=1, n_estimators=100, random_state=None, max_depth=None, min_samples_split=2, **kwargs)
Bases:
ForecasterConfig
Configuration class for bagging Tree-based forecaster model.
- Parameters
max_forecast_steps (
int
) – Max # of steps we would like to forecast for.maxlags (
int
) – Max # of lags for forecastingtarget_seq_index (
Optional
[int
]) – The index of the univariate (amongst all univariates in a general multivariate time series) whose value we would like to forecast.sampling_mode (
str
) – how to process time series data for the tree model. If “normal”, then concatenate all sequences over the window. If “stats”, then give statistics measures over the window. Note: “stats” mode is statistical summary for a multivariate dataset, mainly to reduce the computation cost for high-dimensional time series. For univariate data, it is not necessary to use “stats” instead of the sequence itself as the input. Therefore, for univariate, the model will automatically adopt “normal” mode.prediction_stride (
int
) –the prediction step for training and forecasting
If univariate: the sequence target of the length of prediction_stride will be utilized, forecasting will be done by means of autoregression with the stride unit of prediction_stride
If multivariate:
if = 1: the autoregression with the stride unit of 1
if > 1: only support sequence mode, and the model will set prediction_stride = max_forecast_steps
n_estimators (
int
) – number of base estimators for the tree ensemblerandom_state – random seed for bagging
max_depth – max depth of base estimators
min_samples_split – min split for tree leaves
- class merlion.models.forecast.baggingtrees.BaggingTreeForecaster(config)
Bases:
ForecasterBase
,MultiVariateAutoRegressionMixin
Tree model for multivariate time series forecasting.
- config_class
alias of
BaggingTreeForecasterConfig
- model = None
- property maxlags: int
- Return type
int
- property sampling_mode: str
- Return type
str
- property prediction_stride: int
- Return type
int
- train(train_data, train_config=None)
Trains the forecaster on the input time series.
- Parameters
train_data (
TimeSeries
) – aTimeSeries
of metric values to train the model.train_config – Additional training configs, if needed. Only required for some models.
- Returns
the model’s prediction on
train_data
, in the same format as if you calledForecasterBase.forecast
on the time stamps oftrain_data
- forecast(time_stamps, time_series_prev=None, return_iqr=False, return_prev=False)
Returns the model’s forecast on the timestamps given. Note that if
self.transform
is specified in the config, the forecast is a forecast of transformed values! It is up to you to manually invert the transform if desired.- Parameters
time_stamps (
List
[int
]) – Either alist
of timestamps we wish to forecast for, or the number of steps (int
) we wish to forecast for.time_series_prev (
Optional
[TimeSeries
]) – a list of (timestamp, value) pairs immediately precedingtime_series
. If given, we use it to initialize the time series model. Otherwise, we assume thattime_series
immediately follows the training data.return_iqr – whether to return the inter-quartile range for the forecast. Note that not all models support this option.
return_prev – whether to return the forecast for
time_series_prev
(and its stderr or IQR if relevant), in addition to the forecast fortime_stamps
. Only used iftime_series_prev
is provided.
- Returns
(forecast, forecast_stderr)
ifreturn_iqr
is false,(forecast, forecast_lb, forecast_ub)
otherwise.forecast
: the forecast for the timestamps givenforecast_stderr
: the standard error of each forecast value.May be
None
.
forecast_lb
: 25th percentile of forecast values for each timestampforecast_ub
: 75th percentile of forecast values for each timestamp
- set_data_already_transformed()
- reset_data_already_transformed()
- class merlion.models.forecast.baggingtrees.RandomForestForecasterConfig(max_forecast_steps, maxlags, target_seq_index=None, sampling_mode='normal', prediction_stride=1, n_estimators=100, random_state=None, max_depth=None, min_samples_split=2, **kwargs)
Bases:
BaggingTreeForecasterConfig
Configuration class for bagging Tree-based forecaster model.
- Parameters
max_forecast_steps (
int
) – Max # of steps we would like to forecast for.maxlags (
int
) – Max # of lags for forecastingtarget_seq_index (
Optional
[int
]) – The index of the univariate (amongst all univariates in a general multivariate time series) whose value we would like to forecast.sampling_mode (
str
) – how to process time series data for the tree model. If “normal”, then concatenate all sequences over the window. If “stats”, then give statistics measures over the window. Note: “stats” mode is statistical summary for a multivariate dataset, mainly to reduce the computation cost for high-dimensional time series. For univariate data, it is not necessary to use “stats” instead of the sequence itself as the input. Therefore, for univariate, the model will automatically adopt “normal” mode.prediction_stride (
int
) –the prediction step for training and forecasting
If univariate: the sequence target of the length of prediction_stride will be utilized, forecasting will be done by means of autoregression with the stride unit of prediction_stride
If multivariate:
if = 1: the autoregression with the stride unit of 1
if > 1: only support sequence mode, and the model will set prediction_stride = max_forecast_steps
n_estimators (
int
) – number of base estimators for the tree ensemblerandom_state – random seed for bagging
max_depth – max depth of base estimators
min_samples_split – min split for tree leaves
- class merlion.models.forecast.baggingtrees.RandomForestForecaster(config)
Bases:
BaggingTreeForecaster
Random Forest Regressor for time series forecasting
Random Forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset, and uses averaging to improve the predictive accuracy and control over-fitting.
- config_class
alias of
RandomForestForecasterConfig
- class merlion.models.forecast.baggingtrees.ExtraTreesForecasterConfig(max_forecast_steps, maxlags, target_seq_index=None, sampling_mode='normal', prediction_stride=1, n_estimators=100, random_state=None, max_depth=None, min_samples_split=2, **kwargs)
Bases:
BaggingTreeForecasterConfig
Configuration class for bagging Tree-based forecaster model.
- Parameters
max_forecast_steps (
int
) – Max # of steps we would like to forecast for.maxlags (
int
) – Max # of lags for forecastingtarget_seq_index (
Optional
[int
]) – The index of the univariate (amongst all univariates in a general multivariate time series) whose value we would like to forecast.sampling_mode (
str
) – how to process time series data for the tree model. If “normal”, then concatenate all sequences over the window. If “stats”, then give statistics measures over the window. Note: “stats” mode is statistical summary for a multivariate dataset, mainly to reduce the computation cost for high-dimensional time series. For univariate data, it is not necessary to use “stats” instead of the sequence itself as the input. Therefore, for univariate, the model will automatically adopt “normal” mode.prediction_stride (
int
) –the prediction step for training and forecasting
If univariate: the sequence target of the length of prediction_stride will be utilized, forecasting will be done by means of autoregression with the stride unit of prediction_stride
If multivariate:
if = 1: the autoregression with the stride unit of 1
if > 1: only support sequence mode, and the model will set prediction_stride = max_forecast_steps
n_estimators (
int
) – number of base estimators for the tree ensemblerandom_state – random seed for bagging
max_depth – max depth of base estimators
min_samples_split – min split for tree leaves
- class merlion.models.forecast.baggingtrees.ExtraTreesForecaster(config)
Bases:
BaggingTreeForecaster
Extra Trees Regressor for time series forecasting
Extra Trees Regressor implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.
- config_class
alias of
ExtraTreesForecasterConfig
merlion.models.forecast.bo0stingtrees module
Boosting Tree-based models for multivariate time series forecasting. LightGBM
- class merlion.models.forecast.boostingtrees.BoostingTreeForecasterConfig(max_forecast_steps, maxlags, target_seq_index=None, sampling_mode='normal', prediction_stride=1, learning_rate=0.1, n_estimators=100, random_state=None, max_depth=None, n_jobs=-1, **kwargs)
Bases:
ForecasterConfig
Configuration class for boosting Tree-based forecaster model.
- Parameters
max_forecast_steps (
int
) – Max # of steps we would like to forecast for.target_seq_index (
Optional
[int
]) – The index of the univariate (amongst all univariates in a general multivariate time series) whose value we would like to forecast.maxlags (
int
) – Max # of lags for forecastingsampling_mode (
str
) – how to process time series data for the tree model. If “normal”, then concatenate all sequences over the window. If “stats”, then give statistics measures over the window. Note: “stats” mode is statistical summary for a multivariate dataset, mainly to reduce the computation cost for high-dimensional time series. For univariate data, it is not necessary to use “stats” instead of the sequence itself as the input. Therefore, for univariate, the model will automatically adopt “normal” mode.prediction_stride (
int
) –the prediction step for training and forecasting
If univariate: the sequence target of the length of prediction_stride will be utilized, forecasting will be done by means of autoregression with the stride unit of prediction_stride
If multivariate:
if = 1: the autoregression with the stride unit of 1
if > 1: only support sequence mode, and the model will set prediction_stride = max_forecast_steps
learning_rate – learning rate for boosting
n_estimators – number of base estimators for the tree ensemble
random_state – random seed for boosting
max_depth – max depth of base estimators
n_jobs – num of threading, -1 or 0 indicates device default, positive int indicates num of threads
- class merlion.models.forecast.boostingtrees.BoostingTreeForecaster(config)
Bases:
ForecasterBase
,MultiVariateAutoRegressionMixin
Tree model for multivariate time series forecasting.
- config_class
alias of
BoostingTreeForecasterConfig
- model = None
- property maxlags: int
- Return type
int
- property sampling_mode: str
- Return type
str
- property prediction_stride: int
- Return type
int
- train(train_data, train_config=None)
Trains the forecaster on the input time series.
- Parameters
train_data (
TimeSeries
) – aTimeSeries
of metric values to train the model.train_config – Additional training configs, if needed. Only required for some models.
- Returns
the model’s prediction on
train_data
, in the same format as if you calledForecasterBase.forecast
on the time stamps oftrain_data
- forecast(time_stamps, time_series_prev=None, return_iqr=False, return_prev=False)
Returns the model’s forecast on the timestamps given. Note that if
self.transform
is specified in the config, the forecast is a forecast of transformed values! It is up to you to manually invert the transform if desired.- Parameters
time_stamps (
List
[int
]) – Either alist
of timestamps we wish to forecast for, or the number of steps (int
) we wish to forecast for.time_series_prev (
Optional
[TimeSeries
]) – a list of (timestamp, value) pairs immediately precedingtime_series
. If given, we use it to initialize the time series model. Otherwise, we assume thattime_series
immediately follows the training data.return_iqr – whether to return the inter-quartile range for the forecast. Note that not all models support this option.
return_prev – whether to return the forecast for
time_series_prev
(and its stderr or IQR if relevant), in addition to the forecast fortime_stamps
. Only used iftime_series_prev
is provided.
- Returns
(forecast, forecast_stderr)
ifreturn_iqr
is false,(forecast, forecast_lb, forecast_ub)
otherwise.forecast
: the forecast for the timestamps givenforecast_stderr
: the standard error of each forecast value.May be
None
.
forecast_lb
: 25th percentile of forecast values for each timestampforecast_ub
: 75th percentile of forecast values for each timestamp
- set_data_already_transformed()
- reset_data_already_transformed()
- class merlion.models.forecast.boostingtrees.LGBMForecasterConfig(max_forecast_steps, maxlags, target_seq_index=None, sampling_mode='normal', prediction_stride=1, learning_rate=0.1, n_estimators=100, random_state=None, max_depth=None, n_jobs=-1, **kwargs)
Bases:
BoostingTreeForecasterConfig
Configuration class for boosting Tree-based forecaster model.
- Parameters
max_forecast_steps (
int
) – Max # of steps we would like to forecast for.target_seq_index (
Optional
[int
]) – The index of the univariate (amongst all univariates in a general multivariate time series) whose value we would like to forecast.maxlags (
int
) – Max # of lags for forecastingsampling_mode (
str
) – how to process time series data for the tree model. If “normal”, then concatenate all sequences over the window. If “stats”, then give statistics measures over the window. Note: “stats” mode is statistical summary for a multivariate dataset, mainly to reduce the computation cost for high-dimensional time series. For univariate data, it is not necessary to use “stats” instead of the sequence itself as the input. Therefore, for univariate, the model will automatically adopt “normal” mode.prediction_stride (
int
) –the prediction step for training and forecasting
If univariate: the sequence target of the length of prediction_stride will be utilized, forecasting will be done by means of autoregression with the stride unit of prediction_stride
If multivariate:
if = 1: the autoregression with the stride unit of 1
if > 1: only support sequence mode, and the model will set prediction_stride = max_forecast_steps
learning_rate – learning rate for boosting
n_estimators – number of base estimators for the tree ensemble
random_state – random seed for boosting
max_depth – max depth of base estimators
n_jobs – num of threading, -1 or 0 indicates device default, positive int indicates num of threads
- class merlion.models.forecast.boostingtrees.LGBMForecaster(config)
Bases:
BoostingTreeForecaster
Light gradient boosting (LGBM) regressor for time series forecasting
LightGBM is a light weight and fast gradient boosting framework that uses tree based learning algorithms, for more details, please refer to the document https://lightgbm.readthedocs.io/en/latest/Features.html
- config_class
alias of
LGBMForecasterConfig
merlion.models.forecast.lstm module
A forecaster based on a LSTM neural net.
- class merlion.models.forecast.lstm.LSTMConfig(max_forecast_steps, target_seq_index=None, nhid=1024, model_strides=(1,), **kwargs)
Bases:
ForecasterConfig
Config object used to define a forecaster model.
Configuration class for
LSTM
.- Parameters
max_forecast_steps (
int
) – Max # of steps we would like to forecast for.target_seq_index (
Optional
[int
]) – The index of the univariate (amongst all univariates in a general multivariate time series) whose value we would like to forecast.nhid – hidden dimension of LSTM
model_strides – tuple indicating the stride(s) at which we would like to subsample the input data before giving it to the model.
- class merlion.models.forecast.lstm.LSTMTrainConfig(lr=1e-05, batch_size=128, epochs=128, seq_len=256, data_stride=1, valid_split=0.2, checkpoint_file='checkpoint.pt')
Bases:
object
LSTM training configuration.
- class merlion.models.forecast.lstm.LSTM(config)
Bases:
ForecasterBase
LSTM forecaster: this assume the input time series has equal intervals across all its values so that we can use sequence modeling to make forecast.
- config_class
alias of
LSTMConfig
- train(train_data, train_config=None)
Trains the forecaster on the input time series.
- Parameters
train_data (
TimeSeries
) – aTimeSeries
of metric values to train the model.train_config (
Optional
[LSTMTrainConfig
]) – Additional training configs, if needed. Only required for some models.
- Return type
Tuple
[TimeSeries
,None
]- Returns
the model’s prediction on
train_data
, in the same format as if you calledForecasterBase.forecast
on the time stamps oftrain_data
- forecast(time_stamps, time_series_prev=None, return_iqr=False, return_prev=False)
Returns the model’s forecast on the timestamps given. Note that if
self.transform
is specified in the config, the forecast is a forecast of transformed values! It is up to you to manually invert the transform if desired.- Parameters
time_stamps (
Union
[int
,List
[int
]]) – Either alist
of timestamps we wish to forecast for, or the number of steps (int
) we wish to forecast for.time_series_prev (
Optional
[TimeSeries
]) – a list of (timestamp, value) pairs immediately precedingtime_series
. If given, we use it to initialize the time series model. Otherwise, we assume thattime_series
immediately follows the training data.return_iqr – whether to return the inter-quartile range for the forecast. Note that not all models support this option.
return_prev – whether to return the forecast for
time_series_prev
(and its stderr or IQR if relevant), in addition to the forecast fortime_stamps
. Only used iftime_series_prev
is provided.
- Return type
Tuple
[TimeSeries
,None
]- Returns
(forecast, forecast_stderr)
ifreturn_iqr
is false,(forecast, forecast_lb, forecast_ub)
otherwise.forecast
: the forecast for the timestamps givenforecast_stderr
: the standard error of each forecast value.May be
None
.
forecast_lb
: 25th percentile of forecast values for each timestampforecast_ub
: 75th percentile of forecast values for each timestamp