merlion.models.automl package

Contains all AutoML layers.

`base`	Base class/mixin for AutoML hyperparameter search.
`seasonality`	Automatic seasonality detection.
`autoets`	Automatic hyperparamter selection for ETS.
`autoprophet`	Automatic hyperparameter selection for Facebook's Prophet.
`autosarima`	Automatic hyperparameter selection for SARIMA.

Submodules

merlion.models.automl.base module

Base class/mixin for AutoML hyperparameter search.

class merlion.models.automl.base.AutoMLMixIn(config=None, model=None, **kwargs)

Bases: LayeredModel

Abstract base class which converts LayeredModel into an AutoML model.

train_model(train_data, train_config=None, **kwargs)

Generates a set of candidate models and picks the best one.

Parameters

train_data (TimeSeries) – the data to train on.
train_config – the train config of the underlying model (optional).

abstract generate_theta(train_data)

Parameters: train_data (TimeSeries) – Pre-processed training data to use for generation of hyperparameters \(\theta\)

Returns an iterator of hyperparameter candidates for consideration with th underlying model.

Return type: Iterator

abstract evaluate_theta(thetas, train_data, train_config=None, **kwargs)

Parameters

thetas (Iterator) – Iterator of the hyperparameter candidates
train_data (TimeSeries) – Pre-processed training data
train_config – Training configuration

Return the optimal hyperparameter, as well as optionally a model and result of the training procedure.

Return type: Tuple[Any, Optional[ModelBase], Optional[Tuple[TimeSeries, Optional[TimeSeries]]]]

abstract set_theta(model, theta, train_data=None)

Parameters

model – Underlying base model to which the new theta is applied
theta – Hyperparameter to apply
train_data (Optional[TimeSeries]) – Pre-processed training data (Optional)

Sets the hyperparameter to the provided model. This is used to apply the \(\theta\) to the model, since this behavior is custom to every model. Oftentimes in internal implementations, model is the optimal model.

class merlion.models.automl.base.InformationCriterion(value)

Bases: Enum

An enumeration.

AIC = 1

Akaike information criterion. Computed as

\[\mathrm{AIC} = 2k - 2\mathrm{ln}(L)\]

where k is the number of parameters, and L is the model’s likelihood.

BIC = 2

Bayesian information criterion. Computed as

\[k \mathrm{ln}(n) - 2 \mathrm{ln}(L)\]

where n is the sample size, k is the number of parameters, and L is the model’s likelihood.

AICc = 3

Akaike information criterion with correction for small sample size. Computed as

\[\mathrm{AICc} = \mathrm{AIC} + \frac{2k^2 + 2k}{n - k - 1}\]

where n is the sample size, and k is the number of paramters.

class merlion.models.automl.base.ICConfig(information_criterion=InformationCriterion.AIC, transform=None, **kwargs)

Bases: Config

Mix-in to add an information criterion parameter to a model config.

Parameters

information_criterion (InformationCriterion) – information criterion to select the best model.
transform – Transformation to pre-process input time series.

property information_criterion

class merlion.models.automl.base.ICAutoMLForecaster(config=None, model=None, **kwargs)

Bases: AutoMLMixIn, ForecasterBase

AutoML model which uses an information criterion to determine which model paramters are best.

config_class: alias of ICConfig

property information_criterion

abstract get_ic(model, train_data, train_result)

Returns the information criterion of the model based on the given training data & the model’s train result.

Parameters

model – One of the models being tried. Must be trained.
train_data (DataFrame) – The target sequence of the training data as a pandas.DataFrame.
train_result (Tuple[DataFrame, Optional[DataFrame]]) – The result of calling model._train().

Return type

float

Returns

The information criterion evaluating the model’s goodness of fit.

evaluate_theta(thetas, train_data, train_config=None, **kwargs)

Parameters

thetas (Iterator) – Iterator of the hyperparameter candidates
train_data (TimeSeries) – Pre-processed training data
train_config – Training configuration

Return the optimal hyperparameter, as well as optionally a model and result of the training procedure.

Return type: Tuple[Any, ModelBase, Tuple[TimeSeries, Optional[TimeSeries]]]

merlion.models.automl.autosarima module

Automatic hyperparameter selection for SARIMA.

class merlion.models.automl.autosarima.AutoSarimaConfig(model=None, auto_seasonality=True, periodicity_strategy=PeriodicityStrategy.ACF, auto_pqPQ=True, auto_d=True, auto_D=True, maxiter=None, max_k=100, max_dur=3600, approximation=None, approx_iter=None, pval=0.05, max_lag=None, model_kwargs=None, transform=None, **kwargs)

Bases: SeasonalityConfig

Configuration class for AutoSarima. Acts as a wrapper around a Sarima model, which automatically detects the seasonality, (seasonal) differencing order, and (seasonal) AR/MA orders. If a non-numeric value is specified for any of the relevant parameters in the order or seasonal order, we assume that the user wishes to detect that parameter automatically.

Note

The automatic selection of AR, MA, seasonal AR, and seasonal MA parameters is implemented in a coupled way. The user must specify all of these parameters explicitly to avoid automatic selection.

Parameters

model (Union[Sarima, dict, None]) – The model being wrapped, or a dict representing it.
auto_seasonality (bool) – Whether to automatically detect the seasonality.
periodicity_strategy (PeriodicityStrategy) – Periodicity Detection Strategy.
auto_pqPQ (bool) – Whether to automatically choose AR/MA orders p, q and seasonal AR/MA orders P, Q.
auto_d (bool) – Whether to automatically choose the difference order d.
auto_D (bool) – Whether to automatically choose the seasonal difference order D.
maxiter (Optional[int]) – The maximum number of iterations to perform
max_k (int) – Maximum number of models considered in the stepwise search
max_dur (float) – Maximum training time considered in the stepwise search
approximation (Optional[bool]) – Whether to use approx_iter iterations (instead of maxiter) to speed up computation. If None, we use approximation mode when the training data is too long (>150), or when the length off the period is too high (periodicity > 12).
approx_iter (Optional[int]) – The number of iterations to perform in approximation mode
pval – p-value for deciding whether a detected seasonality is statistically significant.
max_lag – max lag considered for seasonality detection.
model_kwargs – Keyword arguments used specifically to initialize the underlying model. Only used if model is a dict. Will override keys in the model dict if specified.
transform – Transformation to pre-process input time series.
kwargs – Any other keyword arguments (e.g. for initializing a base class). If model is a dict, we will also try to pass these arguments when creating the actual underlying model. However, they will not override arguments in either the model dict or model_kwargs dict.

property order

property seasonal_order

class merlion.models.automl.autosarima.AutoSarima(config=None, model=None, **kwargs)

Bases: SeasonalityLayer

config_class: alias of AutoSarimaConfig

generate_theta(train_data)

generate [action, theta]. action is an indicator for stepwise seach (stepwsie) of p, q, P, Q, trend parameters or use a predefined parameter combination (pqPQ) theta is a list of parameter combination [order, seasonal_order, trend]

Return type: Iterator

evaluate_theta(thetas, train_data, train_config=None, **kwargs)

Parameters

thetas (Iterator) – Iterator of the hyperparameter candidates
train_data (TimeSeries) – Pre-processed training data
train_config – Training configuration

Return the optimal hyperparameter, as well as optionally a model and result of the training procedure.

Return type: Tuple[Any, Optional[Sarima], Optional[Tuple[TimeSeries, Optional[TimeSeries]]]]

set_theta(model, theta, train_data=None)

Parameters

model – Underlying base model to which the new theta is applied
theta – Hyperparameter to apply
train_data (Optional[TimeSeries]) – Pre-processed training data (Optional)

Sets the hyperparameter to the provided model. This is used to apply the \(\theta\) to the model, since this behavior is custom to every model. Oftentimes in internal implementations, model is the optimal model.

merlion.models.automl.seasonality module

Automatic seasonality detection.

class merlion.models.automl.seasonality.PeriodicityStrategy(value)

Bases: Enum

Strategy to choose the seasonality if multiple candidates are detected.

ACF = 1: Select the seasonality value with the highest autocorrelation.

Min = 2: Select the minimum seasonality.

Max = 3: Select the maximum seasonality.

All = 4: Use all seasonalities. Only valid for models which support multiple seasonalities.

class merlion.models.automl.seasonality.SeasonalityModel

Bases: object

Class provides simple implementation to set the seasonality in a model. Extend this class to implement custom behavior for seasonality processing.

abstract 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 (UnivariateTimeSeries) – Training data (or numpy array representing the target univariate) for any model-specific adjustments you might want to make.

class merlion.models.automl.seasonality.SeasonalityConfig(model, periodicity_strategy=PeriodicityStrategy.ACF, pval=0.05, max_lag=None, model_kwargs=None, transform=None, **kwargs)

Bases: LayeredModelConfig

Config object for an automatic seasonality detection layer.

Parameters

model – The model being wrapped, or a dict representing it.
periodicity_strategy – Strategy to choose the seasonality if multiple candidates are detected.
pval (float) – p-value for deciding whether a detected seasonality is statistically significant.
max_lag – max lag considered for seasonality detection.
model_kwargs – Keyword arguments used specifically to initialize the underlying model. Only used if model is a dict. Will override keys in the model dict if specified.
transform – Transformation to pre-process input time series.
kwargs – Any other keyword arguments (e.g. for initializing a base class). If model is a dict, we will also try to pass these arguments when creating the actual underlying model. However, they will not override arguments in either the model dict or model_kwargs dict.

property multi_seasonality

Returns: Whether the model supports multiple seasonalities. False unless explicitly overridden.

property periodicity_strategy: PeriodicityStrategy

Return type: PeriodicityStrategy
Returns: Strategy to choose the seasonality if multiple candidates are detected.

class merlion.models.automl.seasonality.SeasonalityLayer(config=None, model=None, **kwargs)

Bases: AutoMLMixIn

Seasonality Layer that uses automatically determines the seasonality of your data. Can be used directly on any model that implements SeasonalityModel class. The algorithmic idea is from the theta method. We find a set of multiple candidate seasonalites, and we return the best one(s) based on the PeriodicityStrategy.

config_class: alias of SeasonalityConfig

property require_even_sampling: bool

Whether the model assumes that training data is sampled at a fixed frequency

Return type: bool

property require_univariate: Whether the model only works with univariate time series.

property multi_seasonality

Returns: Whether the model supports multiple seasonalities.

property periodicity_strategy

Returns: Strategy to choose the seasonality if multiple candidates are detected.

property pval

Returns: p-value for deciding whether a detected seasonality is statistically significant.

property max_lag

Returns: max_lag for seasonality detection

set_theta(model, theta, train_data=None)

Parameters

model – Underlying base model to which the new theta is applied
theta – Hyperparameter to apply
train_data (Optional[TimeSeries]) – Pre-processed training data (Optional)

Sets the hyperparameter to the provided model. This is used to apply the \(\theta\) to the model, since this behavior is custom to every model. Oftentimes in internal implementations, model is the optimal model.

evaluate_theta(thetas, train_data, train_config=None, **kwargs)

Parameters

thetas (Iterator) – Iterator of the hyperparameter candidates
train_data (TimeSeries) – Pre-processed training data
train_config – Training configuration

Return the optimal hyperparameter, as well as optionally a model and result of the training procedure.

Return type: Tuple[Any, Optional[ModelBase], Optional[Tuple[TimeSeries, Optional[TimeSeries]]]]

generate_theta(train_data)

Parameters: train_data (TimeSeries) – Pre-processed training data to use for generation of hyperparameters \(\theta\)

Returns an iterator of hyperparameter candidates for consideration with th underlying model.

Return type: Iterator