ARIMA¶

path: statsmodels.tsa.arima_model.Arima

description: this is an Autoregressive Integrated Moving Average (ARIMA) prediction model.

argument	type	description
parameters
`X`	`numpy.ndarray`	n-dimensional array containing the input sequences for the model
hyperparameters
`steps`	`int`	number of forward steps to predict
`p`	`int`	the number of autoregressive parameters to use
`d`	`int`	the number of differences to use
`q`	`int`	the number of moving average (MA) parameters to use
output
`y`	`numpy.ndarray`	predicted values

In [1]: import numpy as np

In [2]: from mlstars import load_primitive

In [3]: X = np.array(range(100)).reshape(-1, 1)

In [4]: primitive = load_primitive('statsmodels.tsa.arima_model.Arima',
   ...:     arguments={"X": X, "steps": 1, })
   ...: 

In [5]: primitive.produce(X=X)
Out[5]: array([99.99999478])

MinMaxScaler LSTM