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        "author": "Vladimir Shulyak",
        "author_email": "vladimir@shulyak.net",
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            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
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        "description": "# ```ts-eval``` Time Series analysis and evaluation tools\n\n[![pypi](https://img.shields.io/pypi/v/ts-eval)](https://pypi.org/project/ts-eval/)\n[![Build Status](https://travis-ci.org/vshulyak/ts-eval.svg?branch=master)](https://travis-ci.org/vshulyak/ts-eval)\n[![codecov](https://codecov.io/github/vshulyak/ts-eval/branch/master/graph/badge.svg)](https://codecov.io/github/vshulyak/ts-eval)\n[![python3](https://img.shields.io/pypi/pyversions/ts-eval)](https://www.python.org/downloads/release/python-374/)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![License: MIT](https://img.shields.io/pypi/l/ts-eval)](https://github.com/vshulyak/ts-eval/blob/master/LICENSE)\n[![Contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/vshulyak/ts-eval/issues)\n\n---\nA set of tools to make time series analysis easier.\n\n## \ud83e\udde9 Current features\n\n* **N-step ahead rolling origin time series evaluation** \u2013 using a Jupyter widget.\n* **Friedman / Nemenyi rank test (posthoc)** \u2013 to see which model statistically performs better.\n* **Relative Metrics** \u2013 rMSE, rMAE + Forecasted Value analogues.\n* **Prediction Interval Metrics** \u2013 MIS, rMIS, FVrMIS\n* **Fixed fourier series generation**  \u2013 fixed in time according to pandas index\n* **Naive/Seasonal models for baseline predictions** (with prediction intervals)\n* **Statsmodels n-step evaluation** \u2013 helper functions to evaluate n-step ahead forecasts using Statsmodels models or naive/seasonal naive models.\n\n## \ud83d\udc69\ud83c\udffe\u200d\ud83c\udfa8 Widget Preview\n\nIn:\n```\nTSMetrics(target, sm_seas, default)\n.use_reference(snaive)\n.for_horizons(0, 1, 5, 23)\n.for_time_slices(time_slices.all, time_slices.weekend)\n.with_description()\n.with_prediction_rankings(mtx.FVrMSE, mtx.FVrMIS)\n.with_predictions_plot()\n.show()\n```\n\nOut:\n![Demo Screenshot](images/demo_screenshot.png)\n\n## \ud83d\udc69\ud83c\udffe\u200d\ud83d\ude92 Demo\nFor a more elaborate example, please check out the [Demo Notebook](https://nbviewer.jupyter.org/github/vshulyak/ts-eval/blob/master/examples/basic_usage.ipynb).\n\nAlternatively, check out [interactive Binder demo](https://mybinder.org/v2/gh/vshulyak/ts-eval/master?filepath=examples%2Fbasic_usage.ipynb)\n\n## \ud83e\udd26\ud83c\udffe\u200d Motivation\n\nWhile working on a long term time series analysis project, I had a need to summarize and store performance metrics\nof different models and compare them. As it's daunting to do this across dozens of notebooks, I huddled over some code\nto do what I want in a few lines of code.\n\n## \ud83d\udc69\ud83c\udffe\u200d\ud83d\ude80 Installation\n\n      pip install ts-eval\n\n\n## \ud83d\udccb Release Planning:\n\n* **Release 0.3**\n  * use pandas better for dataframe styles / viz https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html\n  * api like (viz1 | viz2 | viz3 ) / viz4 (patchwork R package)\n  * CRPS evaluation\n  * dynamic insample forecast for statsmodels\n  * PI coverage estimation is really needed (in %)\n  * predictions based on loess decomposition https://github.com/jrmontag/STLDecompose\n  * dataset description: first index - last index sample (dates or ids)\n  * transform with callback\n  * altair-like API where you can combine components with +\n  * success rate of prediction intervals like here http://freerangestats.info/blog/2016/01/30/hybrid-forecasts\n  * describe case when MIS rankings are better for one dataset, but its mean is worse (due to huge outliers)\n  * wrapper around xarray datasets, which always returns non-NaN data and/or statistics that I need are computed\n    inside of this wrapper. NaNs are always inside. Doable?\n  * boxplots by timestep visualization (with boxplot, outliers for each step)\n  * remove collection of deps in style [tests_and_bla_bla] to [tests,bla]\n  * links to papers \u2013 AvgRelMAE (Davydenko and Fildes, 2013); link to Nemenyi paper / implementations\n  * make graphs with PIs more narrow on 0,1,.. steps as there's too much space left (with an option to turn this off).\n  * better API for the end user \u2013 minimize interaction with xarray\n  * pep517 build / wheels / better setup.py as per Hynek\n  * travis: add 3.8 default python when it's available\n  * docs: supported metrics & API options\n  * Maybe use api like Summary in statsmodels MLEModel class, it has extend methods and warn/info messages\n  * pretty legend for lots like here https://studywolf.wordpress.com/2017/11/21/matplotlib-legends-for-mean-and-confidence-interval-plots/\n  * Look for TODOs\n  * changable colors\n  * turn off colored display option\n  * a nicer API for raw metrics container\n  * codacy badge\n  * boxplots to compare models (as an alternative)\n  * violin plots to compare predictions \u2013 areas can be colored, different metrics on left and right (like relative...)\n* **Release 0.4**\n  * holiday/fourier features model\n  * fix viz module to have less of important stuff\n  * a gif with project visualization\n  * check shapes of input arrays (target vs preds), now it doesn't raise an error\n  * Baseline prediction using target dataset (without explicit calculation, but losing some time points)\n\n## \ud83d\udca1 Ideas\n\n* components\n  * Graph: Visualize outliers from confidence interval\n  * Multi-comparison component: scikit_posthocs lib or homecooked?\n  * inspect true confidence interval coverage via sampling (was done in postings around bayesian dropout sampling)\n  * xarrays: compare if compared datasets are actually equal (offets by dates, shapes, maybe even hashing)\n  * bin together step performance, like steps 0-1, 2-5, 6-12, 13-24\n  * highlight regions using a mask (holidays, etc.)\n  * option to view interactively points using widget (plotly)?\n  * diagnostics: bias to over / underestimate points\n  * animated graphs for change in seasonality\n* features\n  * example notebook for fourier?\n  * tests for fourier\n  * nint generation\n* utils:\n  * model adaptor (for different models, generic) which generates 3d prediction dataset. For stastmodels using dyn forecast or kalman filter\n  * future importance calculator, but only if I can manipulate input features\n  * feature selection using PACF / prewhiten?\n* project\n  * more defensive style (add arg checks, so it's easier to understand what is going on)\n  * docstrings\n  * circleci\n  * https://timothycrosley.github.io/portray/ for docs\n* sMAPE & MASE can be added for the jupyter evaluation tables\n* ? Residual stats: since I have residuals => Ljung-Box, Heteroscedasticity test, Jarque-Bera \u2013 like in statsmodels results,\n  but probably these stats were inspected already by the user... and on which step should they be computed then?\n\n## See also\n\n* https://arch.readthedocs.io/en/latest/multiple-comparison/multiple-comparison_examples.html\n* RMC as an anlogue for nemenyi https://github.com/config-i1/greybox/blob/master/R/rmc.R\n\n## \ud83e\udd39\ud83c\udffc\u200d\u2642\ufe0f Development\n\nRecommended development workflow:\n```\npipenv install -e .[dev]\npipenv shell\n```\nThe library doesn't use Flit/Poetry, so the suggested workflow is based on Pipenv (as per https://github.com/pypa/pipenv/issues/1911).\nPipfile* are ignored in the .gitignore.\n# Changelog\n\n##  0.2.3 (2021-06-03)\n\n### Fixes (unreleased changes from 2019, doh)\n\n- Fix results for 1 time series (noop)\n\n\n##  0.2.2 (2019-10-22)\n\n### Fixes\n\n- Fix nan values propagated to Friedman Nemenyi test.\n- Critical distance is returned alongside Friedman Nemenyi test.\n\n\n##  0.2.1 (2019-10-18)\n\n### Fixes\n\nOutdated import in wheel version of the package.\n\n\n##  0.2.0 (2019-10-16)\n\n### Features\n\n- Multiple prediction ranking with Friedman Nemenyi posthoc.\n- Visualization of prediction intervals\n- Indication of prediction ranking in a colorful table\n- Rewrite of the internal computation machinery\n\n\n##  0.1.0 (2019-10-04)\n\n### Features\n\n- N-step ahead evaluation widget for Jupyter\n- Absolute & relative metrics for point forecasts and prediction intervals (MSE, MAE, rMSE, rMAE, MIS, rMIS)\n- Naive/Seasonal models for baseline (with prediction intervals)\n- Helper functions to evaluate n-step ahead forecasts using Statsmodels models or naive/seasonal naive models.\n- Holiday features generation and model evaluation on holiday datetimes.\n\n\n## 0.0.1 (2019-09-18)\n\n### Features\n\n- Fixed fourier series generation (fixed in time according to pandas index)\n\n\n",
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