Skip to main content

Time Series Binder is a Python library for time series analysis and forecasting. It offers a comprehensive set of tools and models, including Pandas integration, statistical methods, neural networks with Keras, and the NeuralProphet library. With Time Series Binder, you can easily manipulate, visualize, and predict time series data, making it an essential toolkit for researchers and analysts.

Project description

Time Series Binder

Time Series Binder is a Python library for time series analysis and forecasting. It provides a comprehensive set of tools and models to manipulate, visualize, and predict time series data. This library is designed to assist researchers and analysts in performing various time series tasks with ease and efficiency.

Features

  • Integration with Pandas for seamless data manipulation and preprocessing.
  • Statistical methods for analyzing time series data, including trend analysis, seasonality decomposition, and outlier detection.
  • Neural network models powered by Keras for advanced time series forecasting.
  • Integration with the NeuralProphet library for additional forecasting capabilities.
  • Visualization tools for creating insightful plots and visual representations of time series data.
  • Integration with scikit-learn for additional machine learning functionality.

Change Log

1.0.4 (30/05/2023)

  • Modified Documentation
  • Modified classes to adapt ANN model.
  • Created model_utils package.

Project details


Download files

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

Source Distribution

time_series_binder-1.0.4.tar.gz (8.3 kB view details)

Uploaded Source

Built Distribution

time_series_binder-1.0.4-py3-none-any.whl (9.2 kB view details)

Uploaded Python 3

File details

Details for the file time_series_binder-1.0.4.tar.gz.

File metadata

  • Download URL: time_series_binder-1.0.4.tar.gz
  • Upload date:
  • Size: 8.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.2

File hashes

Hashes for time_series_binder-1.0.4.tar.gz
Algorithm Hash digest
SHA256 769831291c51838f539cadbd1de2d01016ed5230d7e01ba33f7af80067c9f2bb
MD5 6be1a4fcf75d36b739e6190a1b1c4de4
BLAKE2b-256 70765f37854eac29a20fe7726a313deb785016a93d918d6cfde41e0b3d31fd65

See more details on using hashes here.

File details

Details for the file time_series_binder-1.0.4-py3-none-any.whl.

File metadata

File hashes

Hashes for time_series_binder-1.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 5ab8f65c7dc462f9a9522fedb43eaa6d388ae3b6d1a945524a82e09e8e6a425b
MD5 e87b97f8c02d92ad6c2fa575543b7351
BLAKE2b-256 baa3114e0c505e09099948357c75f88edb1b1c8d587bc1fe2c726ff332bf4df7

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