Skip to main content

Gradient boosted time series forecasting.

Project description

MFLES

A Specific implementation from ThymeBoost written with the help of Numba.

Here is a quick benchmark vs AutoETS from M4: alt text

Quick Start:

Install via pip

pip install MFLES

Import MFLES class

from MFLES.Forecaster import MFLES

Import data

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = pd.read_csv(r'https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv')

Fit and predict!

mfles = MFLES()
fitted = mfles.fit(df['Passengers'].values, seasonal_period=12)
predicted = mfles.predict(12)

plt.plot(np.append(fitted, predicted))
plt.plot(df['Passengers'].values)
plt.show()

alt text

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

MFLES-0.1.5-py3-none-any.whl (11.1 kB view details)

Uploaded Python 3

File details

Details for the file MFLES-0.1.5-py3-none-any.whl.

File metadata

  • Download URL: MFLES-0.1.5-py3-none-any.whl
  • Upload date:
  • Size: 11.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.15

File hashes

Hashes for MFLES-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 590c5a7cc6041c80689347650580368b7b820570142a193ff35f84f6640297ff
MD5 c737dc262e8b9ad1140bd9348c2335a9
BLAKE2b-256 c8fc2c674aec12e95f94e7365150484255caa9c9ca58e87a31357f4130b3c4f0

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page