pypsf 1.0.2

Python implementation of the Pattern Sequence Based Forecasting (PSF) algorithm

PyPSF

This project provides a python implementation of the Pattern Sequence Based Forecasting (PSF) algorithm. For a detailed description of the PSF algorithm and some of the practical issues I encountered when using it, see this PDF file.

Installation

pip install pypsf

Dependencies

• scikit-learn
• numpy

Example Usage

import numpy as np

from pypsf import Psf


t_series = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3])
train = t_series[:6]
test = t_series[6:]

psf = Psf(cycle_length=3)
psf.fit(train)

pred = psf.predict(len(test))

print(test) # [1 2 3]
print(pred) # [1. 2. 3.]

Parameters

class Psf

• cycle_length: int
  The cycle length c
• k: int (optional), default None
  The user-defined number of desired clusters when running K-means on the cycles
• w: int (optional), default None
  The user-defined window size
• suppress_warnings: bool (optional), default False
  Suppress all warnings
• apply_diff: bool (optional), default False
  Apply first order differencing to the time series before applying PSF
• diff_periods: int (optional), default 1
  Periods to shift for calculating difference, to allow for either ordinary or seasonal differencing. Ignore if apply_diff=False
• detrend: bool (optional), default False
  Remove a linear trend from the series prior to applying PSF by fitting a simple linear regression model. The trend is subsequently re-added to the predictions.

Psf.fit

• data:
  The input time series
• k_values: iterable[int] (optional), default tuple(range(3, 12))
  The set of candidate values of k to test when finding the "best" k number of clusters based on the training data
• w_values: iterable[int] (optional), default tuple(range(1, 20))
  The set of candidate values of w to test when finding the "best" window size w based on the training data

Psf.predict

• n_ahead: int
  The number of values to predict