{"id":7678,"date":"2025-02-24T13:42:48","date_gmt":"2025-02-24T12:42:48","guid":{"rendered":"https:\/\/www.m2hycon.de\/?p=7678"},"modified":"2025-02-24T16:40:09","modified_gmt":"2025-02-24T15:40:09","slug":"effortless-time-series-feature-generation-a-practical-approach-for-simple-models","status":"publish","type":"post","link":"https:\/\/www.m2hycon.de\/en\/news\/data-science\/effortless-time-series-feature-generation-a-practical-approach-for-simple-models\/","title":{"rendered":"Effort\u00adless Time Series Feature Genera\u00adtion: A Practical Approach for Simple Models"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"7678\" class=\"elementor elementor-7678 elementor-7580\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8f0c233 elementor-section-full_width elementor-section-height-min-height red noisy elementor-section-height-default elementor-section-items-middle\" data-id=\"8f0c233\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-463b507\" data-id=\"463b507\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-25f6583 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"25f6583\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b9e5a52\" data-id=\"b9e5a52\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-aed6906 elementor-widget elementor-widget-theme-post-title elementor-page-title elementor-widget-heading\" data-id=\"aed6906\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"theme-post-title.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Effort\u00adless Time Series Feature Genera\u00adtion: A Practical Approach for Simple Models<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-75a26ec elementor-widget elementor-widget-post-info\" data-id=\"75a26ec\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"post-info.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul class=\"elementor-inline-items elementor-icon-list-items elementor-post-info\">\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-repeater-item-db030de elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text elementor-post-info__item elementor-post-info__item--type-custom\">\n\t\t\t\t\t\t\t\t\t\tVon Mark Willoughby\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t<li class=\"elementor-icon-list-item elementor-repeater-item-2efc209 elementor-inline-item\" itemprop=\"datePublished\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text elementor-post-info__item elementor-post-info__item--type-date\">\n\t\t\t\t\t\t\t\t\t\t<time>February 24, 2025<\/time>\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-f562ac5 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f562ac5\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-1460892\" data-id=\"1460892\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fb960c3 elementor-widget__width-auto elementor-widget elementor-widget-text-editor\" data-id=\"fb960c3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4>Share post:<\/h4>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6a47970 elementor-share-buttons--view-icon elementor-share-buttons--skin-minimal elementor-share-buttons--color-custom elementor-widget__width-auto elementor-share-buttons--shape-circle elementor-grid-0 elementor-widget elementor-widget-share-buttons\" data-id=\"6a47970\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"share-buttons.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-grid\" role=\"list\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_facebook\" role=\"button\" tabindex=\"0\" aria-label=\"Share on facebook\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<i class=\"fab fa-facebook\" aria-hidden=\"true\"><\/i>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_twitter\" role=\"button\" tabindex=\"0\" aria-label=\"Share on twitter\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<i class=\"fab fa-twitter\" aria-hidden=\"true\"><\/i>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_linkedin\" role=\"button\" tabindex=\"0\" aria-label=\"Share on linkedin\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<i class=\"fab fa-linkedin\" aria-hidden=\"true\"><\/i>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_xing\" role=\"button\" tabindex=\"0\" aria-label=\"Share on xing\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<i class=\"fab fa-xing\" aria-hidden=\"true\"><\/i>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-529b227 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"529b227\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c8728fe\" data-id=\"c8728fe\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2fb29bd maths elementor-widget-tablet__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"2fb29bd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Among all the steps involved in time series analysis and forecas\u00adting, <strong>feature genera\u00adtion <\/strong>is one of the most important. Time series features are able to capture the trend, seaso\u00adna\u00adlity, lags, or rolling statis\u00adtics that help the model compre\u00adhend temporal dynamics. <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9888052 maths elementor-widget-tablet__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"9888052\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In this post, we will demons\u00adtrate how to use some practical and simple techni\u00adques for time series feature genera\u00adtion. We will avoid deep learning compli\u00adca\u00adtions and focus on <strong>effec\u00adtive, simple approa\u00adches<\/strong> to create features that will empower simpler models to deliver robust explainable predic\u00adtions. <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-acc3328 elementor-widget-tablet__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"acc3328\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li><a href=\"#Header1\"><strong>How to generate features: Feature enginee\u00adring vs. deep learning methods?<\/strong><\/a><\/li>\n \t<li><a href=\"#Header2\"><strong>Time Series Feature based Genera\u00adtors<\/strong><\/a><\/li>\n \t<li><a href=\"#Header3\"><strong>UseCase: Predic\u00adting the shutdown of a water pump<\/strong><\/a><\/li>\n \t<li><a href=\"#Header4\"><strong>Create features with  <code>Functime<\/code><\/strong><\/a><\/li>\n \t<li><a href=\"#Header5\"><strong>Model predic\u00adtion with the generated Time Series features<\/strong><\/a><\/li>\n \t<li><a href=\"#Header6\"><strong>Our conclu\u00adsion<\/strong><\/a><\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ca253ca elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ca253ca\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e504551\" data-id=\"e504551\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8391c3e elementor-widget elementor-widget-spacer\" data-id=\"8391c3e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a587c7 elementor-widget elementor-widget-heading\" data-id=\"5a587c7\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"Header1\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span>How to generate features: Feature enginee\u00adring vs. deep learning methods?<\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fa4b2bb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fa4b2bb\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9edef60\" data-id=\"9edef60\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-36bfec8 maths elementor-widget elementor-widget-text-editor\" data-id=\"36bfec8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>While deep learning models can automa\u00adti\u00adcally learn complex repre\u00adsen\u00adta\u00adtions from raw time series data and can be trained directly with limited or even no feature trans\u00adfor\u00adma\u00adtion, more classical models such as linear regres\u00adsion and decision trees rely heavily on well-engineered features. These models perform best when they are fed with meaningful features extra\u00adcted from the raw data; feature enginee\u00adring is then an essen\u00adtial ingre\u00addient for good model perfor\u00admance and explaina\u00adbi\u00adlity. <\/p>\n<p><span style=\"color: inherit; font-style: inherit; font-weight: inherit; background-color: var(--ast-global-color-2);\">The good news is that you don\u2019t need a sophisti\u00adcated AI to generate meaningful time series features. Instead, tools and libra\u00adries focused on deter\u00admi\u00adni\u00adstic functional trans\u00adfor\u00adma\u00adtions make it possible to extract them with very little effort: efficient, inter\u00adpr\u00ade\u00adtable methods are ideal for simple machine learning models. <\/span><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-eddcacb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"eddcacb\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9a305b0\" data-id=\"9a305b0\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-08c3c48 elementor-widget elementor-widget-heading\" data-id=\"08c3c48\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"Header2\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span>Python libra\u00adries for genera\u00adting time series Features<\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-66a0462 maths elementor-widget elementor-widget-text-editor\" data-id=\"66a0462\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>A few libra\u00adries are able to automate the process of extra\u00adc\u00adting features from time series data. Among the best known are <strong><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10618-019-00647-x\">Catch22 <\/a><\/strong>which provides 22 carefully curated time series features, and <strong><a href=\"https:\/\/tsfresh.readthedocs.io\/en\/latest\/\">tsfresh <\/a><\/strong>a compre\u00adhen\u00adsive library for extra\u00adc\u00adting a wide range of time series features. While these tools are extre\u00admely powerful, they can be very compu\u00adta\u00adtio\u00adnally inten\u00adsive and are there\u00adfore not as well suited to very large data sets or real-time appli\u00adca\u00adtions common in industry.  <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8f40de9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8f40de9\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-82bbc85\" data-id=\"82bbc85\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e26d429 maths elementor-widget elementor-widget-text-editor\" data-id=\"e26d429\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In this post, we will focus on <code>Functime<\/code>, a light\u00adweight and efficient open-source library for fast feature genera\u00adtion built on Rust and seamlessly integrated with Polars. <code>Functime<\/code> offers an excel\u00adlent balance between compu\u00adta\u00adtional speed and flexi\u00adbi\u00adlity, making it ideal for scena\u00adrios where simpli\u00adcity and perfor\u00admance are crucial. It is optimized to allow effort\u00adless calcu\u00adla\u00adtion of statis\u00adtical features, inclu\u00adding lags, over a desired periodi\u00adcity. With <code>Functime<\/code>, features for simpler models can be generated quickly, without the comple\u00adxity and overhead of more elabo\u00adrate tools.  <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8a61b94 elementor-widget elementor-widget-heading\" data-id=\"8a61b94\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"Header3\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span>UseCase: Predic\u00adting the shutdown of a water pump  <\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-41da160 maths elementor-widget elementor-widget-text-editor\" data-id=\"41da160\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>For this use case, we aim to predict poten\u00adtial shutdowns of a water pump using time series data. The data set consists of raw sensor readings collected at regular inter\u00advals from 52 sensors, together with a timestamp and a status label (<code>machine_status<\/code>). Each sensor records a specific aspect of the pump\u2019s opera\u00adtion, such as pressure, tempe\u00adra\u00adture or flow rate.  <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7bcbc7f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7bcbc7f\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-305d286\" data-id=\"305d286\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-225a5db maths elementor-widget elementor-widget-text-editor\" data-id=\"225a5db\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>The dataset is struc\u00adtured as follows:<\/strong><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-98f0d0b elementor-widget elementor-widget-code-highlight\" data-id=\"98f0d0b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"code-highlight.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"prismjs-tomorrow copy-to-clipboard \">\n\t\t\t<pre data-line class=\"highlight-height language-python line-numbers\">\n\t\t\t\t<code readonly=\"true\" class=\"language-python\">\n\t\t\t\t\t<xmp>timestamp # Zeitstempel jeder Beobachtung\nsensor_00 # Messwerte von Sensor 00\n...\nsensor_51 # Messwerte von Sensor 51\nmaschine_status # Betriebsstatus der Pumpe (NORMAL, WARNUNG oder ABGESCHALTET)<\/xmp>\n\t\t\t\t<\/code>\n\t\t\t<\/pre>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-225bd82 maths elementor-widget elementor-widget-text-editor\" data-id=\"225bd82\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The data for our example comes from the <a href=\"https:\/\/www.kaggle.com\/code\/winternguyen\/water-pump-maintenance-shutdown-prediction\">use case of the same name on Kaggle<\/a><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ef9434d elementor-widget elementor-widget-code-highlight\" data-id=\"ef9434d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"code-highlight.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"prismjs-tomorrow copy-to-clipboard \">\n\t\t\t<pre data-line class=\"highlight-height language-python line-numbers\">\n\t\t\t\t<code readonly=\"true\" class=\"language-python\">\n\t\t\t\t\t<xmp>ts_sensor_path = Path(os.path.abspath(\"\")).parents[1] \/ \"data\" \/ \"ts_data\"\/ \"ts_sensor_data.csv\"\nts_sensor_data = pl.read_csv(source=ts_sensor_path)\nts_sensor_data.head()\n<\/xmp>\n\t\t\t\t<\/code>\n\t\t\t<\/pre>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-451e72e elementor-widget elementor-widget-image\" data-id=\"451e72e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"768\" height=\"264\" src=\"https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/250214_Blog_Zeitreihen_Tabelle-768x264.png\" class=\"attachment-medium_large size-medium_large wp-image-7611\" alt srcset=\"https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/250214_Blog_Zeitreihen_Tabelle-768x264.png 768w, https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/250214_Blog_Zeitreihen_Tabelle-300x103.png 300w, https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/250214_Blog_Zeitreihen_Tabelle-1024x352.png 1024w, https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/250214_Blog_Zeitreihen_Tabelle.png 1116w\" sizes=\"(max-width: 768px) 100vw, 768px\">\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-17b6716 elementor-widget elementor-widget-spacer\" data-id=\"17b6716\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6131bb7 elementor-widget elementor-widget-heading\" data-id=\"6131bb7\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"Header4\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span>Create features with FUNCTIME<\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-debd921 maths elementor-widget-tablet__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"debd921\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>With Functime, we take on the challenges of modeling: the genera\u00adtion of time series features. The tool computed statis\u00adtical features over specific time periods and helps trans\u00adform the original data into a set of intui\u00adtive charac\u00adte\u00adristics. For the purposes of this presen\u00adta\u00adtion, we calcu\u00adlated statis\u00adtical features such as the <strong>absolute maximum<\/strong> and <strong>root mean square<\/strong> for all six hours of data from each sensor. Over <strong>312 features<\/strong> were computed from the data from <strong>52 sensors in just 73 milli\u00adse\u00adconds<\/strong>, showca\u00adsing the effici\u00adency of the feature genera\u00adtion process. This rapid compu\u00adta\u00adtion makes it feasible to handle <strong>real-time or high-frequency sensor data streams without signi\u00adfi\u00adcant compu\u00adta\u00adtional effort<\/strong>. The computed features can be used directly as input for classical models such as Linear Regres\u00adsion, Random Forests, or Gradient Boosted Trees.     <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b78992d elementor-widget elementor-widget-code-highlight\" data-id=\"b78992d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"code-highlight.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"prismjs-tomorrow copy-to-clipboard \">\n\t\t\t<pre data-line class=\"highlight-height language-python line-numbers\">\n\t\t\t\t<code readonly=\"true\" class=\"language-python\">\n\t\t\t\t\t<xmp>def generate_features_for_timeseries(column_name: str) -&gt; dict:\n    ts = pl.col(column_name).ts\n    return {\n        f\"mean_n_absolute_max_{column_name}\": ts.mean_n_absolute_max(n_maxima=3),\n        f\"range_over_mean_{column_name}\": ts.range_over_mean(),\n        f\"root_mean_square_{column_name}\": ts.root_mean_square(),\n        f\"first_location_of_maximum_{column_name}\": ts.first_location_of_maximum(),\n        f\"last_location_of_maximum_{column_name}\": ts.last_location_of_maximum(),\n        f\"absolute_maximum_{column_name}\": ts.absolute_maximum()\n    }\nsensor_columns = [col for col in ts_sensor_data.columns if col not in ['timestamp', 'machine_status', '']]\nnew_features = {\n    feature_name: calculation\n    for sensor_column in sensor_columns\n    for feature_name, calculation in generate_features_for_timeseries(sensor_column).items()\n}\ntimeseries_features = (\n    ts_sensor_data.group_by_dynamic(\n        index_column=\"timestamp\",\n        every=\"6h\",\n        group_by=\"machine_status\",\n        start_by=\"window\"\n    )\n    .agg(**new_features)\n)\ntimeseries_features.head()\n<\/xmp>\n\t\t\t\t<\/code>\n\t\t\t<\/pre>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-85876b3 elementor-widget elementor-widget-image\" data-id=\"85876b3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"768\" height=\"286\" src=\"https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/250219_Blog_Zeitreihen_Tabelle2-768x286.png\" class=\"attachment-medium_large size-medium_large wp-image-7613\" alt srcset=\"https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/250219_Blog_Zeitreihen_Tabelle2-768x286.png 768w, https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/250219_Blog_Zeitreihen_Tabelle2-300x112.png 300w, https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/250219_Blog_Zeitreihen_Tabelle2-1024x381.png 1024w, https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/250219_Blog_Zeitreihen_Tabelle2.png 1144w\" sizes=\"(max-width: 768px) 100vw, 768px\">\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c446653 elementor-widget elementor-widget-spacer\" data-id=\"c446653\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7f47103 maths elementor-widget elementor-widget-text-editor\" data-id=\"7f47103\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The follo\u00adwing graph shows how well our features corre\u00adlate with our targets. These time series features provide valuable insights into the behavior of the sensors and their relati\u00adonship to the pump status (<code>NORMAL<\/code>, <code>BROKEN<\/code> or <code>RECOVERING<\/code>). For example, we can deter\u00admine <strong>that the pump is broken when the root mean square value of sensor 48 <\/strong> is <strong>close to 0 <\/strong>. We also expect that <strong>higher absolute maximum values for sensors 3, 4 and 11 increase the proba\u00adbi\u00adlity<\/strong> that the pump is in a <code>NORMAL<\/code> state.   <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e15e707 elementor-widget elementor-widget-spacer\" data-id=\"e15e707\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4ec82d3 elementor-widget elementor-widget-image\" data-id=\"4ec82d3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"768\" height=\"611\" src=\"https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/ZeitreihenBlog_FeatureTaret_plots-768x611.png\" class=\"attachment-medium_large size-medium_large wp-image-7623\" alt srcset=\"https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/ZeitreihenBlog_FeatureTaret_plots-768x611.png 768w, https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/ZeitreihenBlog_FeatureTaret_plots-300x239.png 300w, https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/ZeitreihenBlog_FeatureTaret_plots-1024x814.png 1024w, https:\/\/www.m2hycon.de\/wp-content\/uploads\/2025\/02\/ZeitreihenBlog_FeatureTaret_plots.png 1358w\" sizes=\"(max-width: 768px) 100vw, 768px\">\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3b4c01b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3b4c01b\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c141939\" data-id=\"c141939\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4c2a63d elementor-widget elementor-widget-spacer\" data-id=\"4c2a63d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-da87d62 elementor-widget elementor-widget-heading\" data-id=\"da87d62\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"Header5\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span>Model predic\u00adtions with our time series features<\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9f62c8a maths elementor-widget elementor-widget-text-editor\" data-id=\"9f62c8a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tUsing the computed features, we built a predic\u00adtive model to classify the water pump\u2019s opera\u00adtional status.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d384f00 maths elementor-widget elementor-widget-text-editor\" data-id=\"d384f00\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tLever\u00adaging <code>SelectKBest<\/code>, we select the <strong>30 most important features<\/strong> based on <code>ANOVA F-Statistiken<\/code>. As a base model, we chose a <code>HistGradientBoostingClassifier<\/code> that is robust to unbalanced classes and inher\u00adently provides good predic\u00adtions.\nThis stream\u00adlined approach, powered by light\u00adweight feature genera\u00adtion, shows how classical models can provide high-quality predic\u00adtions when combined with well-engineered time series features. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8f52440 maths elementor-widget elementor-widget-text-editor\" data-id=\"8f52440\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<strong>Our key insights for model use and forecast evalua\u00adtion:<\/strong>\n\n<ul>\n<li>Feature genera\u00adtion lever\u00adages Rust-based <code>functime<\/code> and <code>polars<\/code> data proces\u00adsing libra\u00adries, which make it possible to work with large data sets even on a simple notebook.<\/li>\n<li>The model handles effec\u00adtively class imbalances, achie\u00adving high metrics across all catego\u00adries. This demons\u00adtrates the strength of <code>HistGradientBoostingClassifier<\/code> combined with well-crafted time series features. <\/li>\n<li>Minor perfor\u00admance dips for the <code>RECOVERING<\/code> class indicate possible impro\u00adve\u00adments, such as fine-tuning the model or inclu\u00adding additional features tailored to transi\u00adtion states.<\/li>\n\n\t\t\t\t\t\t\t\t<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9e41e9d elementor-widget elementor-widget-code-highlight\" data-id=\"9e41e9d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"code-highlight.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"prismjs-tomorrow copy-to-clipboard \">\n\t\t\t<pre data-line class=\"highlight-height language-python line-numbers\">\n\t\t\t\t<code readonly=\"true\" class=\"language-python\">\n\t\t\t\t\t<xmp>X = timeseries_features[timeseries_features.columns[2:]]\ny = timeseries_features[\"machine_status\"]\nselector = SelectKBest(score_func=f_classif, k=30).set_output(transform=\"pandas\")\nX_selected = selector.fit_transform(X, y)\n\nX_train, X_test, y_train, y_test = train_test_split(X_selected, y, test_size=0.3, random_state=42, stratify=y)\nmodel = HistGradientBoostingClassifier(random_state=42, class_weight=\"balanced\")\nmodel.fit(X_train, y_train)\ny_pred = model.predict(X_test)\n\nreport = classification_report(y_test, y_pred)\nprint(report)\n<\/xmp>\n\t\t\t\t<\/code>\n\t\t\t<\/pre>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7016f0 premium-table-dir-ltr elementor-widget elementor-widget-premium-tables-addon\" data-id=\"a7016f0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"premium-tables-addon.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\n\t\t<div class=\"premium-table-wrap\">\n\t\t\t\n\t\t\t<table class=\"premium-table\" data-settings=\"{&quot;sort&quot;:false,&quot;usNumbers&quot;:false,&quot;sortMob&quot;:false,&quot;search&quot;:false,&quot;records&quot;:false,&quot;dataType&quot;:&quot;custom&quot;,&quot;csvFile&quot;:null,&quot;firstRow&quot;:null,&quot;separator&quot;:null,&quot;pagination&quot;:&quot;&quot;,&quot;rows&quot;:0}\">\n\n\t\t\t\n\t\t<thead class=\"premium-table-head\">\n\n\t\t\t<tr class=\"premium-table-row\">\n\n\t\t\t\t<th class=\"premium-table-cell elementor-repeater-item-d8e2645\"><span class=\"premium-table-text\"><\/span><\/th><th class=\"premium-table-cell elementor-repeater-item-79831b7\"><span class=\"premium-table-text\">PRECISION<\/span><\/th><th class=\"premium-table-cell elementor-repeater-item-187ffef\"><span class=\"premium-table-text\">recall<\/span><\/th><th class=\"premium-table-cell elementor-repeater-item-c007fc3\"><span class=\"premium-table-text\">F1-SCORE<\/span><\/th><th class=\"premium-table-cell elementor-repeater-item-662002b\"><span class=\"premium-table-text\">N (Support)<\/span><\/th>\n\t\t\t<\/tr>\n\n\t\t<\/thead>\n\n\t\t\t\t<tbody class=\"premium-table-body\">\n\t\t\t\t\t\t<tr class=\"premium-table-row elementor-repeater-item-d55d70b\"><td class=\"premium-table-cell elementor-repeater-item-8c9707f\"><span class=\"premium-table-text\">BROKEN<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-c4f3fc7\"><span class=\"premium-table-text\">1.00<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-08a14bd\"><span class=\"premium-table-text\">1.00<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-286933b\"><span class=\"premium-table-text\">1.00<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-5e20f5d\"><span class=\"premium-table-text\">2<\/span><\/td><\/tr><tr class=\"premium-table-row elementor-repeater-item-2bb4746\"><td class=\"premium-table-cell elementor-repeater-item-6dab24d\"><span class=\"premium-table-text\">NORMAL<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-735e912\"><span class=\"premium-table-text\">0.99<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-15c5276\"><span class=\"premium-table-text\">1.00<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-7759a46\"><span class=\"premium-table-text\">1.00<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-ca70c75\"><span class=\"premium-table-text\">346<\/span><\/td><\/tr><tr class=\"premium-table-row elementor-repeater-item-e870985\"><td class=\"premium-table-cell elementor-repeater-item-85d6932\"><span class=\"premium-table-text\">RECOVE\u00adRING<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-df299b9\"><span class=\"premium-table-text\">0.96<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-1ba28b9\"><span class=\"premium-table-text\">0.92<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-f4dece4\"><span class=\"premium-table-text\">0.94<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-8fa06fa\"><span class=\"premium-table-text\">26<\/span><\/td><\/tr><tr class=\"premium-table-row elementor-repeater-item-1f4e850\"><td class=\"premium-table-cell elementor-repeater-item-b609c04\"><span class=\"premium-table-text\"><\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-bf6fb00\"><span class=\"premium-table-text\"><\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-3c8521d\"><span class=\"premium-table-text\"><\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-5a0b4ef\"><span class=\"premium-table-text\"><\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-d8c1543\"><span class=\"premium-table-text\"><\/span><\/td><\/tr><tr class=\"premium-table-row elementor-repeater-item-6d5d61d\"><td class=\"premium-table-cell elementor-repeater-item-3b48df0\"><span class=\"premium-table-text\">accuracy<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-73905d3\"><span class=\"premium-table-text\"><\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-79762a3\"><span class=\"premium-table-text\"><\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-74c2345\"><span class=\"premium-table-text\">0.99<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-e5a3307\"><span class=\"premium-table-text\">374<\/span><\/td><\/tr><tr class=\"premium-table-row elementor-repeater-item-6d2e420\"><td class=\"premium-table-cell elementor-repeater-item-d6737a7\"><span class=\"premium-table-text\">macro avg<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-71c839c\"><span class=\"premium-table-text\">0.98<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-606167e\"><span class=\"premium-table-text\">0.97<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-92abbc8\"><span class=\"premium-table-text\">0.98<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-e87f2d5\"><span class=\"premium-table-text\">374<\/span><\/td><\/tr><tr class=\"premium-table-row elementor-repeater-item-66148ba\"><td class=\"premium-table-cell elementor-repeater-item-11378ca\"><span class=\"premium-table-text\">weighted avg<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-4b7681a\"><span class=\"premium-table-text\">0.99<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-38965d0\"><span class=\"premium-table-text\">0.99<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-e4ab57f\"><span class=\"premium-table-text\">0.99<\/span><\/td><td class=\"premium-table-cell elementor-repeater-item-1e3c54b\"><span class=\"premium-table-text\">374<\/span><\/td>\t\t\t<\/tr>\n\t\t<\/tbody>\n\n\t\t\n\t\t\t<\/table>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-05cbda5 elementor-widget elementor-widget-spacer\" data-id=\"05cbda5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ae62aa6 maths elementor-widget elementor-widget-text-editor\" data-id=\"ae62aa6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>With stream\u00adlined feature genera\u00adtion, the model demons\u00adtrates excep\u00adtio\u00adnally promi\u00adsing perfor\u00admance across all classes.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1daffe0 maths elementor-widget elementor-widget-text-editor\" data-id=\"1daffe0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<strong>Our inter\u00adpre\u00adta\u00adtion of the model perfor\u00admance with the generated time series features:  <\/strong>\n<ul>\n \t<li><strong>BROKEN<\/strong>: The model makes <strong>perfect predic\u00adtions<\/strong> with precision, recall and F1 score of 1.00, but may not be very reliable as there are only two examples (support = 2);<\/li>\n \t<li><strong>NORMAL<\/strong>: The model is <strong>almost perfect<\/strong> with 99% precision and 100% recall, which shows that almost all normal examples have been correctly identi\u00adfied;<\/li>\n \t<li><strong>RECOVE\u00adRING<\/strong>: There was a slight drop in perfor\u00admance (F1 score = 0.94) due to some false negatives, suggesting that <strong>impro\u00adve\u00adments <strong>are possible<\/strong> through feature enginee\u00adring or hyper\u00adpa\u00adra\u00admeter tuning <\/strong>.  <\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4afb230 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"4afb230\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-27012de elementor-widget elementor-widget-heading\" data-id=\"27012de\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"Header6\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span>Conclu\u00adsion: Functime package as a real help for time series features <\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-88a3d7d maths elementor-widget elementor-widget-text-editor\" data-id=\"88a3d7d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The Python package <code>Functime<\/code> can really make life easier by creating the time series features in a matter of seconds. For our model for predic\u00adting the function\u00ada\u00adlity of water pumps, the perfor\u00admance was already really promi\u00adsing without us having to do any time-consuming fine tuning. Another advan\u00adtage of automated feature creation is, of course, that no feature is acciden\u00adtally forgotten and the proce\u00addure can be easily repeated with new or extended data.   <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-43b90c4 elementor-widget elementor-widget-spacer\" data-id=\"43b90c4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-db6d9e3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"db6d9e3\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4d90837\" data-id=\"4d90837\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fd345a5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fd345a5\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-3454126\" data-id=\"3454126\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f782047 elementor-widget elementor-widget-author-box\" data-id=\"f782047\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"author-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-author-box\">\n\t\t\t\t\t\t\t<div class=\"elementor-author-box__avatar\">\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.m2hycon.de\/wp-content\/uploads\/2023\/09\/m2hycon_ueberuns_team_Mark_Willoughby_800x800-300x300.jpg\" alt=\"Picture of Mark Willhoughby\" loading=\"lazy\">\n\t\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"elementor-author-box__text\">\n\t\t\t\t\t\t\t\t\t<div>\n\t\t\t\t\t\t<h4 class=\"elementor-author-box__name\">\n\t\t\t\t\t\t\tMark Willhoughby\t\t\t\t\t\t<\/h4>\n\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-author-box__bio\">\n\t\t\t\t\t\t<p>Data Scien\u00adtist<\/p>\n\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-e562604 elementor-hidden-tablet elementor-hidden-mobile\" data-id=\"e562604\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-eeab2a1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"eeab2a1\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-af34f0d\" data-id=\"af34f0d\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b74183e elementor-widget elementor-widget-post-navigation\" data-id=\"b74183e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"post-navigation.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-post-navigation\" role=\"navigation\" aria-label=\"Post Navigation\">\n\t\t\t<div class=\"elementor-post-navigation__prev elementor-post-navigation__link\">\n\t\t\t\t<a href=\"https:\/\/www.m2hycon.de\/en\/news\/expert-knowledge\/from-data-chaos-to-efficiency-prisma-orm-benefits-for-developers\/\" rel=\"prev\"><span class=\"post-navigation__arrow-wrapper post-navigation__arrow-prev\"><i aria-hidden=\"true\" class=\"fas fa-angle-left\"><\/i><span class=\"elementor-screen-only\">Prev<\/span><\/span><span class=\"elementor-post-navigation__link__prev\"><span class=\"post-navigation__prev--label\">Previous post<\/span><span class=\"post-navigation__prev--title\">From data chaos to effici\u00adency: Prisma ORM benefits for develo\u00adpers<\/span><\/span><\/a>\t\t\t<\/div>\n\t\t\t\t\t\t<div class=\"elementor-post-navigation__next elementor-post-navigation__link\">\n\t\t\t\t<a href=\"https:\/\/www.m2hycon.de\/en\/news\/what-every-ceo-should-know-about-introducing-ai-in-the-company\/\" rel=\"next\"><span class=\"elementor-post-navigation__link__next\"><span class=\"post-navigation__next--label\">Next post<\/span><span class=\"post-navigation__next--title\">What every CEO should know about intro\u00addu\u00adcing AI in the company<\/span><\/span><span class=\"post-navigation__arrow-wrapper post-navigation__arrow-next\"><i aria-hidden=\"true\" class=\"fas fa-angle-right\"><\/i><span class=\"elementor-screen-only\">Next<\/span><\/span><\/a>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ca82da6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ca82da6\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-09de419\" data-id=\"09de419\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Good features for a time series analysis can also be created automa\u00adti\u00adcally. We test it out with the Python package Functime.   <\/p>\n","protected":false},"author":11,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"wp_typography_post_enhancements_disabled":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[206],"tags":[],"class_list":["post-7678","post","type-post","status-publish","format-standard","hentry","category-data-science"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.5 (Yoast SEO v28.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Generate time series features automatically in Python<\/title>\n<meta name=\"description\" content=\"Boost the performance of simple models with automated time series features. 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