##############################################################################
# Author: Bernard Hernandez
# Filename: adfuller.py
#
# Description : This file contains a wrapper for the adfuller module.
###############################################################################
# https://mkaz.blog/code/python-string-format-cookbook/
# Future
from __future__ import division
# Libraries
import sys
import numpy as np
import pandas as pd
# Import base wrapper
from pyamr.core.stats.wbase import BaseWrapper
[docs]class KPSSWrapper(BaseWrapper):
"""
The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test is used to identify
whether a time series is stationary around a deterministic trend (thus
trend stationary) against the alternative of a unit root.
In the KPSS test, the absence of a unit root is not a proof of stationarity
but, by design, of trend stationarity. This is an important distinction since
it is possible for a time series to be non-stationary, have no unit root yet
be trend-stationary.
In both, unit-root and trend-stationary processes, the mean can be increasing
or decreasing over time; however, in the presence of a shock, trend-stationary
processes revert to this mean tendency in the long run (deterministic trend)
while unit-root processes have a permanent impact (stochastic trend).
====== =========================== =====================================
H Hypothesis Stationarity
====== =========================== =====================================
**H0** The series has no unit root ``Trend-stationary``
**H1** The series has a unit root ``No Trend-Stationary``
====== =========================== =====================================
| If p-value > alpha: Failed to reject H0
| If p-value <= alpha: Reject H0
"""
pass