merlion.models.automl package

Contains all AutoML layers.

`base`	Base class/mixin for AutoML hyperparameter search.
`seasonality`	Automatic seasonality detection.
`autoets`	Automatic seasonality detection for ETS.
`autoprophet`	Automatic (multi)-seasonality detection 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

Base Interface for Implemented AutoML Layers

This abstract class contains all of the methods that Layers should implement. Ideally, these would be generated by an existing mix-in.

config_class: alias of LayeredModelConfig

train(train_data, **kwargs)

Trains the model on the specified time series, optionally with some additional implementation-specific config options train_config.

Parameters

train_data (TimeSeries) – a TimeSeries to use as a training set
train_config – additional configurations (if needed)

abstract generate_theta(train_data)

Parameters: train_data (TimeSeries) – 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)

Parameters

thetas (Iterator) – Iterator of the hyperparameter candidates
train_data (TimeSeries) – 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[ForecasterBase], 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]) – 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.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, 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) – Strategy to choose the seasonality if multiple candidates are detected.
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.
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

require_even_sampling = True

require_univariate = True

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)

Parameters

thetas (Iterator) – Iterator of the hyperparameter candidates
train_data (TimeSeries) – 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]) – 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, 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.
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.

to_dict(_skipped_keys=None)

Returns: dict with keyword arguments used to initialize the config class.

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

Bases: AutoMLMixIn

Seasonality Layer that uses AutoSARIMA-like methods to determine seasonality of your data. Can be used directly on any model that implements SeasonalityModel class.

config_class: alias of SeasonalityConfig

require_even_sampling = False

property require_univariate

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

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.

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]) – 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)

Parameters

thetas (Iterator) – Iterator of the hyperparameter candidates
train_data (TimeSeries) – 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) – Training data to use for generation of hyperparameters \(\theta\)

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

Return type: Iterator