{"id":6170,"date":"2025-06-11T08:11:51","date_gmt":"2025-06-11T08:11:51","guid":{"rendered":"https:\/\/hetida.io\/blog\/platform-feature-low-pass-filter\/"},"modified":"2026-03-30T09:19:51","modified_gmt":"2026-03-30T09:19:51","slug":"platform-feature-low-pass-filter","status":"publish","type":"post","link":"https:\/\/hetida.io\/en\/blog\/platform-feature-low-pass-filter\/","title":{"rendered":"Platform feature: Low pass filter"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"6170\" class=\"elementor elementor-6170 elementor-6118\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3dd1030f e-flex e-con-boxed e-con e-parent\" data-id=\"3dd1030f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1d7f98cb elementor-widget elementor-widget-heading\" data-id=\"1d7f98cb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.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\">Platform feature: Low pass filter<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3c0f0bcd elementor-widget elementor-widget-text-editor\" data-id=\"3c0f0bcd\" 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 IoT and analytics platform hetida platform offers a wide range of standard workflows for efficient data pre-processing. One of these is the low-pass filter &#8211; a proven method for smoothing out high-frequency fluctuations in time series while preserving the low-frequency trend in the data. In this article, we show how the filter works and how it can be specifically applied to water level data in water management.  <\/p>\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-35ce730 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"35ce730\" 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-38fee304 elementor-widget elementor-widget-heading\" data-id=\"38fee304\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Initial situation<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6e49dec6 elementor-widget elementor-widget-text-editor\" data-id=\"6e49dec6\" 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\tThe Ruhrverband measures the <a href=\"https:\/\/pegelonline.wsv.de\/gast\/stammdaten?pegelnr=2769510000100\" target=\"_blank\" rel=\"noopener\">water level of the Ruhr in Hattingen<\/a> every 15 minutes. Such data is interesting both for flood early warning systems and for determining the frequency of low water due to drought. However, short-term fluctuations occur in the 15-minute raw data, for example due to passing ships, blocking branches or measurement errors. Such fluctuations can affect the further hydrological use of the data. One method of smoothing out high-frequency fluctuations while retaining the low-frequency tendency of the data is a low-pass filter. The hetida platform &#8211; fuseki&#8217;s data analysis tool with connected dashboarding system &#8211; is well suited for this: it provides <a href=\"https:\/\/hetida.io\/en\/agents-alarms-and-workflows\/\" target=\"_blank\" rel=\"noopener\">standard workflows<\/a> for data pre-processing, including high-pass and low-pass filters. In the following, we use the hetida platform to smooth the level data of the Ruhr with a low-pass filter and thus prepare it for further analysis.      \n\nTo do this, we first illustrate the basic functionality of a low-pass filter using artificially generated data. We then load the level data from the Ruhr in Hattingen into the hetida platform and demonstrate the hetida platform&#8217;s low-pass filter workflow on real data. We visualize the result in a dashboard of the platform.  \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-f4b95aa elementor-widget elementor-widget-heading\" data-id=\"f4b95aa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How the low pass filter works<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3643b49 elementor-widget elementor-widget-text-editor\" data-id=\"3643b49\" 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\tWe imagine the measurement of a signal (this can be an audio signal, but also any other type of time series) that is composed of two other signals: a low-frequency main signal and a high-frequency interference signal:\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-171eab8f elementor-widget elementor-widget-image\" data-id=\"171eab8f\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Funktionsweise-Tiefpassfilter.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"Funktionsweise Tiefpassfilter\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjEyMSwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDZcL0Z1bmt0aW9uc3dlaXNlLVRpZWZwYXNzZmlsdGVyLndlYnAifQ%3D%3D\">\n\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"2317\" height=\"652\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Funktionsweise-Tiefpassfilter.webp\" class=\"attachment-full size-full wp-image-6121\" alt=\"Abbildung der\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Funktionsweise-Tiefpassfilter.webp 2317w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Funktionsweise-Tiefpassfilter-300x84.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Funktionsweise-Tiefpassfilter-1024x288.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Funktionsweise-Tiefpassfilter-768x216.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Funktionsweise-Tiefpassfilter-1536x432.webp 1536w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Funktionsweise-Tiefpassfilter-2048x576.webp 2048w\" sizes=\"(max-width: 2317px) 100vw, 2317px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-1945583 elementor-widget elementor-widget-text-editor\" data-id=\"1945583\" 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>We are only interested in the low-frequency main signal. However, as we can only measure the composite signal, we have to filter out the high-frequency interference signal.  <strong>This is precisely the purpose of a low pass filter: it filters out high-frequency signals so that only the low frequencies can pass through the filter (hence the name \u201clow pass filter\u201d).<\/strong> The hetida platform has a standard workflow that implements such a low pass filter with a Butterworth filter.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e901856 e-con-full e-flex e-con e-child\" data-id=\"e901856\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-57f61ee e-con-full e-flex e-con e-child\" data-id=\"57f61ee\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-43307dc elementor-widget elementor-widget-image\" data-id=\"43307dc\" 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=\"310\" height=\"132\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Formel-Butterworth-Filter.png\" class=\"attachment-large size-large wp-image-6129\" alt=\"Abbildung des Mathematischen Formel des Butterworth-Filters\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Formel-Butterworth-Filter.png 310w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Formel-Butterworth-Filter-300x128.png 300w\" sizes=\"(max-width: 310px) 100vw, 310px\" \/>\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>\n\t\t<div class=\"elementor-element elementor-element-b4aecd4 e-con-full e-flex e-con e-child\" data-id=\"b4aecd4\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-53e4e74 elementor-alert-success elementor-widget elementor-widget-alert\" data-id=\"53e4e74\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"alert.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-alert\" role=\"alert\">\n\n\t\t\t\t\t\t<span class=\"elementor-alert-title\">Butterworth filter<\/span>\n\t\t\t\n\t\t\t\t\t\t<span class=\"elementor-alert-description\">A Butterworth filter is a filter whose frequency response is defined by the formula on the left. The argument \u03c9 is the incoming frequency as a radian speed. The cut-off frequency is denoted by \u03c9c, i.e. the frequency for which signals with a lower frequency \u03c9 &lt; \u03c9c are to be retained and signals with a higher frequency \u03c9 &gt; \u03c9c are to be filtered out. The parameter n denotes the order of the filter; the higher the order, the steeper the transition from the passband to the stopband in the frequency response. As a result of this formula, low incoming frequencies \u03c9 &lt; \u03c9c are not greatly altered by the filter (|H(\u03c9)| \u2248 1), while high frequencies \u03c9 &gt; \u03c9c are filtered out (|H(\u03c9)| \u2248 0).    <\/span>\n\t\t\t\n\t\t\t\t\t\t<button type=\"button\" class=\"elementor-alert-dismiss\" aria-label=\"Dismiss this alert.\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-info-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 110c23.196 0 42 18.804 42 42s-18.804 42-42 42-42-18.804-42-42 18.804-42 42-42zm56 254c0 6.627-5.373 12-12 12h-88c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h12v-64h-12c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h64c6.627 0 12 5.373 12 12v100h12c6.627 0 12 5.373 12 12v24z\"><\/path><\/svg>\t\t\t<\/button>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-90e6bee elementor-widget elementor-widget-text-editor\" data-id=\"90e6bee\" 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\tIn the hetida platform, we configure the \u201ccut-off frequency\u201d and \u201corder\u201d parameters of the Butterworth filter.We also specify the time unit to which the frequency refers &#8211; for example, periods per second, minute, hour or day.We also specify whether the filter should work on one or both sides:When applied on one side, the filter only takes into account current and previous values for each filtered value.With the two-sided application, on the other hand, both earlier and later values are included in the calculation.   \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-c2af92f elementor-widget elementor-widget-image\" data-id=\"c2af92f\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Tiefpassfilter-in-der-IoT-Plattform-1.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"Tiefpassfilter in der IoT Plattform\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjE1OSwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDZcL1RpZWZwYXNzZmlsdGVyLWluLWRlci1Jb1QtUGxhdHRmb3JtLTEud2VicCJ9\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"2377\" height=\"1187\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Tiefpassfilter-in-der-IoT-Plattform-1.webp\" class=\"attachment-full size-full wp-image-6159\" alt=\"Ein Screenshot aus der hetida platform. Dieser zeigt Anwendung des Tiefpassfilters.\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Tiefpassfilter-in-der-IoT-Plattform-1.webp 2377w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Tiefpassfilter-in-der-IoT-Plattform-1-300x150.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Tiefpassfilter-in-der-IoT-Plattform-1-1024x511.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Tiefpassfilter-in-der-IoT-Plattform-1-768x384.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Tiefpassfilter-in-der-IoT-Plattform-1-1536x767.webp 1536w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Tiefpassfilter-in-der-IoT-Plattform-1-2048x1023.webp 2048w\" sizes=\"(max-width: 2377px) 100vw, 2377px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Configuration of the cut-off frequency and order parameters of the Butterworth filter in the hetida platform.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-051bf8e elementor-widget elementor-widget-text-editor\" data-id=\"051bf8e\" 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\tThe low-frequency main signal in our artificial example has a frequency of 0.1 Hz, the high-frequency interference signal has a frequency of 1 Hz. The cut-off frequency is therefore selected in between, in this case 0.25 Hz. We retain the default settings for the time unit (frequency_as_periods_per_unit = \u201cs\u201d for second), the order of the filter (order = 1) and the two-sided application (\u201cforward_backward\u201d = True). The result looks like this:   \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-1d9b2f6 elementor-widget elementor-widget-image\" data-id=\"1d9b2f6\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"Ergebnis Tiefpassfilter\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjE0MywidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDZcL0VyZ2VibmlzLVRpZWZwYXNzZmlsdGVyLndlYnAifQ%3D%3D\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"2280\" height=\"655\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter.webp\" class=\"attachment-full size-full wp-image-6143\" alt=\"Abbildung der Signale nach Anwendung des Tiefpassfilters\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter.webp 2280w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter-300x86.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter-1024x294.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter-768x221.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter-1536x441.webp 1536w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter-2048x588.webp 2048w\" sizes=\"(max-width: 2280px) 100vw, 2280px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Result of the filter in the artificial example<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-6d30251 elementor-widget elementor-widget-text-editor\" data-id=\"6d30251\" 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\tIn reality, there is never such an ideal signal composed of two perfectly regular signals. We therefore try out the same low-pass filter on a composite signal that also contains random noise. The result is similarly satisfactory:  \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-4370bf8 elementor-widget elementor-widget-image\" data-id=\"4370bf8\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Reales-Ergebnis-Tiefpassfilter.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"Reales Ergebnis Tiefpassfilter\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjE2NiwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDZcL1JlYWxlcy1FcmdlYm5pcy1UaWVmcGFzc2ZpbHRlci53ZWJwIn0%3D\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"2300\" height=\"665\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Reales-Ergebnis-Tiefpassfilter.webp\" class=\"attachment-full size-full wp-image-6166\" alt=\"Abbildung der Signale nach Anwendung des Tiefpassfilters\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Reales-Ergebnis-Tiefpassfilter.webp 2300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Reales-Ergebnis-Tiefpassfilter-300x87.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Reales-Ergebnis-Tiefpassfilter-1024x296.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Reales-Ergebnis-Tiefpassfilter-768x222.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Reales-Ergebnis-Tiefpassfilter-1536x444.webp 1536w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Reales-Ergebnis-Tiefpassfilter-2048x592.webp 2048w\" sizes=\"(max-width: 2300px) 100vw, 2300px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Real result of the filter<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-133a50cc elementor-widget elementor-widget-heading\" data-id=\"133a50cc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Application of a low pass filter to level data in the hetida platform<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2a25995 elementor-widget elementor-widget-text-editor\" data-id=\"2a25995\" 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>We imagine that a hydrologist wants to investigate the frequency and duration of low water events in the Ruhr region. She defines a low water event as a water level falling below a certain level for a longer period of time. If a measured water level rises for a short time due to a single rainfall, this is often of no great significance. Other random factors such as a passing ship or a blocked signal can also cause short-term fluctuations. If the water level briefly exceeds the limit value as a result, this does not change the ongoing low water situation.<\/p><p>The hydrologist therefore wants to smooth her measurement data before analyzing low water events. To do this, she uses a low-pass filter to eliminate such short-term fluctuations. We implement this in the hetida platform below.<br \/>The water level data of the Ruhr in Hattingen, which is measured by the Ruhrverband at 15-minute intervals, is made freely available as a .csv file. After downloading, the .csv file can be easily imported into the hetida platform <a href=\"https:\/\/hetida.io\/en\/interfaces\/\" target=\"_blank\" rel=\"noopener\">(more information on the platform interfaces)<\/a>.<\/p>\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-edf3492 elementor-widget elementor-widget-heading\" data-id=\"edf3492\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Import of raw data via CSV file<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-11fb4d06 elementor-widget elementor-widget-image\" data-id=\"11fb4d06\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/CSV-Import-in-IoT-Plattform.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"CSV Import in IoT Plattform\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjEzNywidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDZcL0NTVi1JbXBvcnQtaW4tSW9ULVBsYXR0Zm9ybS53ZWJwIn0%3D\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1615\" height=\"908\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/CSV-Import-in-IoT-Plattform.webp\" class=\"attachment-full size-full wp-image-6137\" alt=\"Screenshot aus der hetida platform. Er zeigt den Import einer CSV-Datei\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/CSV-Import-in-IoT-Plattform.webp 1615w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/CSV-Import-in-IoT-Plattform-300x169.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/CSV-Import-in-IoT-Plattform-1024x576.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/CSV-Import-in-IoT-Plattform-768x432.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/CSV-Import-in-IoT-Plattform-1536x864.webp 1536w\" sizes=\"(max-width: 1615px) 100vw, 1615px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Import of a CSV file into the hetida platform<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-11fc44a elementor-widget elementor-widget-text-editor\" data-id=\"11fc44a\" 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>Once the data has been successfully imported, a signal containing this data can be created. The hetida platform generates a data preview for each signal created at the corresponding point in the Explorer. Here we can already see that there was a longer period of low water levels in the Ruhr in Hattingen in 2003. From March to November, the water levels were below the average low water level at this measuring point, which is 102 cm. However, there were short-term exceedances of this limit during this period. If low water was defined using such a limit value, the period would not be fully classified as a low water period, which is undesirable.<\/p>\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-9bb4afc elementor-widget elementor-widget-image\" data-id=\"9bb4afc\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Rohdaten-vor-Tiefpassfilter-1.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"Rohdaten vor Tiefpassfilter\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjE2MSwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDZcL1JvaGRhdGVuLXZvci1UaWVmcGFzc2ZpbHRlci0xLndlYnAifQ%3D%3D\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"2350\" height=\"940\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Rohdaten-vor-Tiefpassfilter-1.webp\" class=\"attachment-full size-full wp-image-6161\" alt=\"Ein Screenshot aus der hetida platform. Dieser zeigt die Rohdaten vor der Anwendung des Tiefpassfilters.\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Rohdaten-vor-Tiefpassfilter-1.webp 2350w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Rohdaten-vor-Tiefpassfilter-1-300x120.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Rohdaten-vor-Tiefpassfilter-1-1024x410.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Rohdaten-vor-Tiefpassfilter-1-768x307.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Rohdaten-vor-Tiefpassfilter-1-1536x614.webp 1536w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Rohdaten-vor-Tiefpassfilter-1-2048x819.webp 2048w\" sizes=\"(max-width: 2350px) 100vw, 2350px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Viewing the raw data in the platform <\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-7d25614 elementor-widget elementor-widget-text-editor\" data-id=\"7d25614\" 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>We therefore filter the raw data through a low pass filter. We set the cut-off frequency so that events with a duration of less than two weeks are filtered out. We regard these short events as interference signals. Longer-term events, on the other hand, should be retained.<\/p><p>To do this, we select \u201cd\u201d for days as the unit of frequency. We set the cut-off frequency to 1\/14, i.e. 0.0714.\u00a0<\/p>\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-2769b5d elementor-widget elementor-widget-heading\" data-id=\"2769b5d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Result after applying the low pass filter<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e19e21c elementor-widget elementor-widget-text-editor\" data-id=\"e19e21c\" 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 result of this filter is written to a new signal whose data preview looks like this:<\/p>\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-de7cae5 elementor-widget elementor-widget-image\" data-id=\"de7cae5\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter-Pegeldaten.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"Ergebnis Tiefpassfilter Pegeldaten\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjE0MSwidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDZcL0VyZ2VibmlzLVRpZWZwYXNzZmlsdGVyLVBlZ2VsZGF0ZW4ud2VicCJ9\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"2335\" height=\"955\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter-Pegeldaten.webp\" class=\"attachment-full size-full wp-image-6141\" alt=\"Screenshot aus der hetida platform. Er zeigt das Ergebnis der Daten nach Anwendung des Tiefpassfilters.\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter-Pegeldaten.webp 2335w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter-Pegeldaten-300x123.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter-Pegeldaten-1024x419.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter-Pegeldaten-768x314.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter-Pegeldaten-1536x628.webp 1536w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Ergebnis-Tiefpassfilter-Pegeldaten-2048x838.webp 2048w\" sizes=\"(max-width: 2335px) 100vw, 2335px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Data after application of the low pass filter<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-fe4dd34 elementor-widget elementor-widget-text-editor\" data-id=\"fe4dd34\" 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\tAnd indeed, we can see that the water level adjusted by the low-pass filter remains below the 102 cm mark throughout the entire period from April to December. Finally, we create a <a href=\"https:\/\/hetida.io\/en\/dynamic-dashboards\/\" target=\"_blank\" rel=\"noopener\">dashboard <\/a>to visually check the plausibility of the filter result. The result looks like this:  \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-77973f5 elementor-widget elementor-widget-image\" data-id=\"77973f5\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Dashboard-zur-Plausibilitaetspruefung.webp\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"Dashboard zur Plausibilit\u00e4tspr\u00fcfung\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6NjE0NywidXJsIjoiaHR0cHM6XC9cL2hldGlkYS5pb1wvd3AtY29udGVudFwvdXBsb2Fkc1wvMjAyNVwvMDZcL0Rhc2hib2FyZC16dXItUGxhdXNpYmlsaXRhZXRzcHJ1ZWZ1bmcud2VicCJ9\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"2312\" height=\"965\" src=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Dashboard-zur-Plausibilitaetspruefung.webp\" class=\"attachment-full size-full wp-image-6147\" alt=\"Ein Screenshot aus der hetida platform. Dieser zeigt ein Dashboard, welches die Daten vor und nach der Anwendung des Tiefpassfilters, zeigt\" srcset=\"https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Dashboard-zur-Plausibilitaetspruefung.webp 2312w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Dashboard-zur-Plausibilitaetspruefung-300x125.webp 300w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Dashboard-zur-Plausibilitaetspruefung-1024x427.webp 1024w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Dashboard-zur-Plausibilitaetspruefung-768x321.webp 768w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Dashboard-zur-Plausibilitaetspruefung-1536x641.webp 1536w, https:\/\/hetida.io\/wp-content\/uploads\/2025\/06\/Dashboard-zur-Plausibilitaetspruefung-2048x855.webp 2048w\" sizes=\"(max-width: 2312px) 100vw, 2312px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Visual check of the plausibility of the filter results <\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-61ca5f4 elementor-widget elementor-widget-text-editor\" data-id=\"61ca5f4\" 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\tWe have seen how we can apply a low pass filter to the level data of the Ruhr with just a few clicks using the standard workflows of the hetida platform. Now we can use the data for further analysis, either in the platform or after exporting data with another tool. \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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2f2426e4 e-flex e-con-boxed e-con e-parent\" data-id=\"2f2426e4\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-79aedc0f e-con-full e-flex e-con e-child\" data-id=\"79aedc0f\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-310b88ba elementor-widget elementor-widget-menu-anchor\" data-id=\"310b88ba\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div 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One of these is the low pass filter &#8211; a proven method for smoothing out high-frequency fluctuations in time series while preserving the low-frequency trend in the data. In this article, we show how the filter works and how it can be specifically applied to water level data in water management.  <\/p>\n","protected":false},"author":7,"featured_media":6228,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-6170","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-nicht-kategorisiert"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.9 (Yoast SEO v27.9) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Platform feature: Low pass filter<\/title>\n<meta name=\"description\" content=\"The low pass filter is suitable for data pre-processing. We show how it works and the result after application to real data.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/hetida.io\/en\/blog\/platform-feature-low-pass-filter\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Platform feature: Low pass filter\" \/>\n<meta property=\"og:description\" content=\"The low pass filter is suitable for data pre-processing. 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