slearn 0.0.2

A package linking symbolic representation with sklearn for time series prediction

slearn

A package linking symbolic representation with sklearn for time series prediction

Install the slearn package simply by

$ pip install slearn

Symbolic machine learning prediction

Then, import the package

>>> from slearn import symbolicML, slearn

We can predict any symbolic sequence by choosing the classifiers available in scikit-learn.

>>> string = 'aaaabbbccd'
>>> sbml = symbolicML(classifier_name="MLPClassifier", gap=3, random_seed=0, verbose=0)
>>> x, y = sbml._encoding(string)
>>> pred = sbml.forecasting(x, y, step=5, hidden_layer_sizes=(10,10), learning_rate_init=0.1)
>>> print(pred)
['d', 'b', 'a', 'b', 'b'] # the prediction

Also, you can use it by passing into parameters of dictionary form

>>> string = 'aaaabbbccd'
>>> sbml = symbolicML(classifier_name="MLPClassifier", gap=3, random_seed=0, verbose=0)
>>> x, y = sbml._encoding(string)
>>> params = {'hidden_layer_sizes':(10,10), 'activation':'relu', 'learning_rate_init':0.1}
>>> pred = sbml.forecasting(x, y, step=5, **params)
>>> print(pred)
['d', 'b', 'a', 'b', 'b'] # the prediction

But ensure that parameters are existing in the scikit-learn classifiers.

Prediction with symbolic representation

Load libraries.

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from slearn.slearn import *

time_series = pd.read_csv("Amazon.csv") # load the required dataset, here we use Amazon stock daily close price.
ts = time_series.Close.values

Set the number of symbols you would like to predict.

step = 200

You can select the available classifiers for prediction, and the parameters of the chosen classifier follow the same as the scikit-learn library. We usually deploy ABBA symbolic representation, since it achieves better forecasting against SAX.

Use Gaussian Naive Bayes method:

sl = slearn(series=ts, method='ABBA', 
            gap=10, step=step,
            tol=0.01, alpha=0.2, 
            form='numeric', classifier_name="GaussianNB",
            random_seed=1, verbose=1)
sklearn_params = {'var_smoothing':0.001}
abba_nb_pred = sl.predict(**sklearn_params)

Use neural network models method:

sl = slearn(series=ts, method='ABBA',
            gap=10, step=step,
            tol=0.01, alpha=0.2, 
            form='numeric', classifier_name="MLPClassifier",
            random_seed=1, verbose=1)
sklearn_params = {'hidden_layer_sizes':(20,80), 'learning_rate_init':0.1}
abba_nn_pred = sl.predict(**sklearn_params)

We can plot the prediction,

sns.set_theme(style="whitegrid")
plt.figure(figsize=(25, 9))
sns.set(font_scale=2, style="whitegrid")
sns.lineplot(x=np.arange(0, len(ts)), y= ts, color='c', linewidth=6, label='Time series')
sns.lineplot(x=np.arange(len(ts), len(ts)+min_len), y=abba_nb_pred[:min_len], color='tomato', linewidth=6, label='Prediction (ABBA - GaussianNB)')
sns.lineplot(x=np.arange(len(ts), len(ts)+min_len), y=abba_nn_pred[:min_len], color='darkgreen', linewidth=6, label='Prediction (ABBA - MLPClassifier)')
plt.tight_layout()
plt.tick_params(axis='both', labelsize=25)
plt.show()