We all are well aware of the various types of libraries Python has to offer. We’ll be telling you about one such library knows as PyFlux. The most frequently encountered problems in the Machine learning domain is Time series analysis.
PyFlux is an open-source library in Python explicitly built for working with statistic problems. The library has a superb array of recent statistic models. PyFlux also enables users to have a probabilistic approach the advantage with that is that it gives a more complete picture of uncertainty, which is important for time series tasks such as forecasting.
Installation
The latest release of PyFlux is supported on Python 3.5.
pip install pyflux
Application Interface
The PyFlux API is so concise that it takes a minimal number of steps to conduct the model building process.
Example 1: Getting Started with Time Series
Python3
import pandas as pd import datetime from pandas import Series, DataFrame import pandas_datareader import pandas_datareader.data as web import pyflux as pf import matplotlib.pyplot as plt pandas_datareader.__version__ start = datetime.datetime(2009, 1, 1) end = datetime.datetime(2019, 1, 1) df = web.DataReader('T', "yahoo", start, end) print(df.head()) df.info() |
Output:
Example 2: Visualize the Data
Python3
plt.figure(figsize=(15, 5)) plt.ylabel("Returns") plt.plot(df) plt.show() |
Output:
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