Example: Univariate forecast¶

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This example shows how to use ATOM to work with a univariate time series dataset.

Import the airline dataset from sktime.datasets. This is a small and easy to train dataset that measures monthly totals of international airline passengers from 1949 to 1960.

Load the data¶

In [1]:

# Import packages
import numpy as np
from sktime.datasets import load_airline
from atom import ATOMForecaster

# Import packages import numpy as np from sktime.datasets import load_airline from atom import ATOMForecaster

In [2]:

# Load the data
y = load_airline()

print(y)

# Load the data y = load_airline() print(y)

Analyze the data¶

In [3]:

atom = ATOMForecaster(y, verbose=2, random_state=1)

atom = ATOMForecaster(y, verbose=2, random_state=1)

In [4]:

# Since the dataset contains only the target column, atom.X is empty
atom.X

# Since the dataset contains only the target column, atom.X is empty atom.X

In [5]:

# Examine the dataset
atom.plot_series()

# Examine the dataset atom.plot_series()

In [6]:

atom.plot_qq()

atom.plot_qq()

Seasonality¶

In [7]:

# ATOM has a number of plots to understand the seasonality of the data
with atom.canvas(rows=2, cols=1, sharex=True, vspace=0.01, legend=None):
    atom.plot_acf()
    atom.plot_pacf()

# ATOM has a number of plots to understand the seasonality of the data with atom.canvas(rows=2, cols=1, sharex=True, vspace=0.01, legend=None): atom.plot_acf() atom.plot_pacf()

In [8]:

with atom.canvas(rows=2, cols=1, sharex=True, vspace=0.01, legend=None):
    atom.plot_periodogram()
    atom.plot_fft()

with atom.canvas(rows=2, cols=1, sharex=True, vspace=0.01, legend=None): atom.plot_periodogram() atom.plot_fft()

In [9]:

# It's also possible to compute the seasonality
sp = atom.get_seasonal_period()
sp

# It's also possible to compute the seasonality sp = atom.get_seasonal_period() sp

In [10]:

# And use that seasonality
atom.sp = sp

# And use that seasonality atom.sp = sp

Run the pipeline¶

In [11]:

# Use the regular data cleaning methods to transform the target column
atom.scale(columns=-1)

# Use the regular data cleaning methods to transform the target column atom.scale(columns=-1)

In [12]:

atom.y

atom.y

In [13]:

atom.run(["AutoARIMA", "TBATS", "Prophet"])

atom.run(["AutoARIMA", "TBATS", "Prophet"])

In [14]:

# It's also possible to use regression models for forecast tasks
atom.run("RF")

# It's also possible to use regression models for forecast tasks atom.run("RF")

In [15]:

atom.rf.estimator

atom.rf.estimator

Analyze the results¶

In [16]:

atom.evaluate()

atom.evaluate()

In [17]:

atom.winner.plot_forecast()

atom.winner.plot_forecast()

In [18]:

atom.plot_errors()

atom.plot_errors()