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02_​Inspect_​and_​Remove_​Seasonality

Inspecting and Removing Seasonality - Exercise

This workflow shows the seasonality of time series (energy consumption) in an autocorrelation plot. The seasonality is removed by differencing the time series at the lag where the maximum peak is detected in the autocorrelation plot. As an alternative approach, the time series is decomposed into its trend, first and second seasonalities, and residual. The distribution of energy consumption for each hour is shown in a conditional box plot for both the original and differenced time series.


Data Loading Data Preparation ACF Plot & seasonality removal Hourly Box-Plots Compare Hourly Box-Plots before and after seasonality removal Time Series Analysis02. Inspecting & Removing SeasonalitySummary:In this exercise we'll explore seasonality in the time series using conditional box plotsand the (P)ACF plots.Instructions:1) Run the workflow up through the Missing Value node, this is where we left off in theprevious exercise2) Use the Inspect Seasonality component to look at the ACF and PACF plots of theTime Series. Do we have any Seasonality?3) Use the Remove Seasonality component to remove the seasonality we discovered4) Apply another copy of the Inspect Seasonality component after the removal. Doesthe ACF plot look better?5) Use the Extract Date&Time Fields node to extract the Hour from the timestamp (RowID column) after the Missing Value node6) Use the Number to String node to convert the Hour values into string7) Use the Conditional Box Plot node to visualize the Energy Usage by hour, do we seea pattern?8) Repeat steps 5-7 after the Remove Seasonality component, does it look better?Optional) Use the Decompose Signal component after the Missing Value node andlook at the view convertdate/timeinto Date&Time objectsIntroducemissinghoursEnergyusagedata Missing Value String to Date&Time Column Filter Timestamp Alignment CSV Reader Data Loading Data Preparation ACF Plot & seasonality removal Hourly Box-Plots Compare Hourly Box-Plots before and after seasonality removal Time Series Analysis02. Inspecting & Removing SeasonalitySummary:In this exercise we'll explore seasonality in the time series using conditional box plotsand the (P)ACF plots.Instructions:1) Run the workflow up through the Missing Value node, this is where we left off in theprevious exercise2) Use the Inspect Seasonality component to look at the ACF and PACF plots of theTime Series. Do we have any Seasonality?3) Use the Remove Seasonality component to remove the seasonality we discovered4) Apply another copy of the Inspect Seasonality component after the removal. Doesthe ACF plot look better?5) Use the Extract Date&Time Fields node to extract the Hour from the timestamp (RowID column) after the Missing Value node6) Use the Number to String node to convert the Hour values into string7) Use the Conditional Box Plot node to visualize the Energy Usage by hour, do we seea pattern?8) Repeat steps 5-7 after the Remove Seasonality component, does it look better?Optional) Use the Decompose Signal component after the Missing Value node andlook at the view convertdate/timeinto Date&Time objectsIntroducemissinghoursEnergyusagedata Missing Value String to Date&Time Column Filter Timestamp Alignment CSV Reader

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On NodePit since: 2020-06-05
Last update: 2024-04-25
Created with KNIME version: v4.4.0
Tags: Time SeriesEnergy UsageSeasonalityEducation

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