last_value_predictor

Provides a model, which predicts the last given input value.

class LastValuePredictor(time_series_params: TimeSeriesConfig, model_params: ModelConfig)

Bases: Model

Defines a model, which predicts the previous value.

Inits the model.

Parameters:

• time_series_params – Time-series parameters that affect the training and architecture of models

• model_params – configuration for the model.

Raises:

TypeError – if input_length or output_length is not an integer.

property name: str

Returns the models name.

Returns:

The models name.

predict(data: ndarray[Any, dtype[float64]]) → ndarray[Any, dtype[float64]]

Predicts the next timestamps for every row.

Parameters:

data – 3 dimensional numpy array. First dimension contains time-series. Second dimension contains time steps of a time-series. Third dimension contains the attributes at a single timestep.

Returns:

A 3 dimensional numpy array, with the predicted values.

Example

>>> import numpy as np
>>> from simba_ml.prediction.time_series.models import last_value_predictor
>>> from simba_ml.prediction.time_series.config import time_series_config
>>> train = np.array([[[1,2], [1,2]], [[2,5], [2,6]], [[10, 11], [12,12]]])
>>> train.shape
(3, 2, 2)
>>> model_config = last_value_predictor.LastValuePredictorConfig()
>>> ts_config = time_series_config.TimeSeriesConfig()
>>> ts_config.input_length = 2
>>> model = last_value_predictor.LastValuePredictor(ts_config, model_config)
>>> model.train(train=train)
>>> test_input = np.array([[[10, 10], [20, 20]], [[15, 15], [15, 16]]])
>>> print(test_input)
[[[10 10]
  [20 20]]

 [[15 15]
  [15 16]]]
>>> print(model.predict(test_input))
[[[20 20]]

 [[15 16]]]

train(train: list[numpy.ndarray[Any, numpy.dtype[numpy.float64]]]) → None

Trains the model with the given data.

Parameters:

train – data, that can be used for training.

validate_prediction_input(data: ndarray[Any, dtype[float64]]) → None

Validates the input of the predict function.

Parameters:

data – a single dataframe containing the input data, where the output will be predicted.

Raises:

ValueError – if data has incorrect shape (row length does not equal )

class LastValuePredictorConfig(name: str = 'Last Value Predictor')

Bases: ModelConfig

Defines the configuration for the DenseNeuralNetwork.