Skip to main content

Time Series analysis and evaluation tools

Project description

ts-eval Time Series analysis and evaluation tools

Code style: black pypi

A set of tools to help you analyse time series using Python.

🧩 Current features

  • N-step ahead evaluation widget for Jupyter
  • Absolute & relative metrics for point forecasts and prediction intervals (MSE, MAE, rMSE, rMAE, MIS, rMIS)
  • Fixed fourier series generation (fixed in time according to pandas index)
  • Naive/Seasonal models for baseline (with prediction intervals)
  • Helper functions to evaluate n-step ahead forecasts using Statsmodels models or naive/seasonal naive models.

📋 Release Planning:

  • Release 0.2
    • fix ipynb nbviewer preview
    • holiday/fourier features model
    • fix viz module to have less of important stuff
    • a gif with project visualization
    • check shapes of input arrays (target vs preds), now it doesn't raise an error
    • Baseline prediction using target dataset (without explicit calculation, but losing some time points)
    • Graph: plot confint
    • Nemenyi
    • Residual stats: since I have residuals => Ljung-Box, Heteroscedasticity test, Jarque-Bera – like in statsmodels results.

💡 Ideas

  • components
    • Graph: Visualize outliers from confidence interval
    • Multi-comparison component: scikit_posthocs lib or homecooked?
    • inspect true confidence interval coverage via sampling (was done in postings around bayesian dropout sampling)
    • xarrays: compare if compared datasets are actually equal (offets by dates, shapes, maybe even hashing)
    • bin together step performance, like steps 0-1, 2-5, 6-12, 13-24
    • highlight regions using a mask (holidays, etc.)
    • option to view interactively points using widget (plotly)?
    • diagnostics: bias to over / underestimate points
  • features
    • example notebook for fourier?
    • tests for fourier
    • nint generation
  • utils:
    • model adaptor (for different models, generic) which generates 3d prediction dataset. For stastmodels using dyn forecast or kalman filter
    • future importance calculator, but only if I can manipulate input features
    • feature selection using PACF / prewhiten?
  • project
  • sMAPE & MASE can be added for the jupyter evaluation tables
  • For multiple comparisons: import scikit_posthocs as sp sp.posthoc_nemenyi_friedman(pmm)

🤹🏼‍♂️ Development

Recommended development workflow:

pipenv install -e .[dev]
pipenv shell

The library doesn't use Flit/Poetry, so the suggested workflow is based on Pipenv (as per https://github.com/pypa/pipenv/issues/1911). Pipfile* are ignored in the .gitignore.

Changelog

0.1.0 (2019-10-04)

Features

  • N-step ahead evaluation widget for Jupyter
  • Absolute & relative metrics for point forecasts and prediction intervals (MSE, MAE, rMSE, rMAE, MIS, rMIS)
  • Naive/Seasonal models for baseline (with prediction intervals)
  • Helper functions to evaluate n-step ahead forecasts using Statsmodels models or naive/seasonal naive models.
  • Holiday features generation and model evaluation on holiday datetimes.

0.0.1 (2019-09-18)

Features

  • Fixed fourier series generation (fixed in time according to pandas index)

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

ts-eval-0.1.0.tar.gz (20.6 kB view hashes)

Uploaded source

Built Distribution

ts_eval-0.1.0-py2.py3-none-any.whl (28.4 kB view hashes)

Uploaded py2 py3

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