ts-eval 0.2.0

Time Series analysis and evaluation tools

ts-eval Time Series analysis and evaluation tools

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

🧩 Current features

• N-step ahead time series evaluation – using a Jupyter widget.
• Friedman / Nemenyi rank test (posthoc) – to see which model statistically performs better.
• Relative Metrics – rMSE, rMAE + Forecasted Value analogues.
• Prediction Interval Metrics – MIS, rMIS, FVrMIS
• Fixed fourier series generation – fixed in time according to pandas index
• Naive/Seasonal models for baseline predictions (with prediction intervals)
• Statsmodels n-step evaluation – helper functions to evaluate n-step ahead forecasts using Statsmodels models or naive/seasonal naive models.

Here's how the Jupyter widget looks like:

Check out Demo Notebook.

Installation

  pip install ts-eval

📋 Release Planning:

• Release 0.3
  • travis: add 3.8 default python when it's available
  • docs: supported metrics & API options
  • Maybe use api like Summary in statsmodels MLEModel class, it has extend methods and warn/info messages
  • pretty legend for lots like here https://studywolf.wordpress.com/2017/11/21/matplotlib-legends-for-mean-and-confidence-interval-plots/
  • Look for TODOs
  • changable colors
  • turn off colored display option
  • a nicer API for raw metrics container
  • codacy badge
  • coverage badge
  • travis/circleci badge
  • violin plots to compare predictions – areas can be colored, different metrics on left and right (like relative...)
• Release 0.4
  • names of datasets passed in (now default ordinal numbers)
  • 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

💡 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
  • animated graphs for change in seasonality
• 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
  • more defensive style (add arg checks, so it's easier to understand what is going on)
  • docstrings
  • circleci
  • https://timothycrosley.github.io/portray/ for docs
• sMAPE & MASE can be added for the jupyter evaluation tables
• ? Residual stats: since I have residuals => Ljung-Box, Heteroscedasticity test, Jarque-Bera – like in statsmodels results, but probably these stats were inspected already by the user... and on which step should they be computed then?

🤹🏼‍♂️ 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

0.2.0 (2019-10-16)

Features

• Multiple prediction ranking with Friedman Nemenyi posthoc.
• Visualization of prediction intervals
• Indication of prediction ranking in a colorful table
• Rewrite of the internal computation machinery

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)