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01_​Accessing_​Transforming_​and_​Modeling_​Time_​Series

Accessing, Transforming and Modeling Time Series

This workflow shows how to access time series data, make it equally-spaced, impute missing values, aggregate it at a greater granularity, and explore it visually. After these steps, the time series is decomposed into trend, seasonality, and residual. The residual is modeled with an ARIMA model, and deployment data are saved for testing the model's out-of-sample forecast accuracy.


Visual Exploration - Line Plot, Seasonal Plot, Conditional Box Plot, Lag Plot Inspecting and Transforming Time Series Modeling Time Series Steps from accessing to preprocessing, transforming, andmodeling time series Deploying Time Series Read Sample - Superstore dataAscending by timeOriginal daily datamonthYearly seasonalityIntroduce missing daysSales is 0 wheremissingOnly 2017One value perdayyearTurning point, TrendYear, month, weekas seasonal cyclesSeasonal plotMonthly distributions 2017 for testingdeployment.tableNumber rowsMean(Sales)Seasonality lagslags.tabletraining.tableResidualtrend.pmmlOutliers in residualDecomposed time series Excel Reader (XLS) Sorter Line Plot AggregationGranularity Line Plot Timestamp Alignment Missing Value Date&Time-basedRow Filter GroupBy AggregationGranularity Line Plot Extract Date&TimeFields Line Plot Pivoting ConditionalBox Plot Number To String Decompose Signal Rule-basedRow Splitter Table Writer Extract TableDimension Column Filter Variable toTable Row Table Writer Table Writer Column Filter PMML Writer ARIMA Predictor Line Plot Numeric Scorer Joiner Auto ARIMA Learner Numeric Outliers Line Plot Line color Line color Line color Line color Visual Exploration - Line Plot, Seasonal Plot, Conditional Box Plot, Lag Plot Inspecting and Transforming Time Series Modeling Time Series Steps from accessing to preprocessing, transforming, andmodeling time series Deploying Time Series Read Sample - Superstore dataAscending by timeOriginal daily datamonthYearly seasonalityIntroduce missing daysSales is 0 wheremissingOnly 2017One value perdayyearTurning point, TrendYear, month, weekas seasonal cyclesSeasonal plotMonthly distributions 2017 for testingdeployment.tableNumber rowsMean(Sales)Seasonality lagslags.tabletraining.tableResidualtrend.pmmlOutliers in residualDecomposed time series Excel Reader (XLS) Sorter Line Plot AggregationGranularity Line Plot Timestamp Alignment Missing Value Date&Time-basedRow Filter GroupBy AggregationGranularity Line Plot Extract Date&TimeFields Line Plot Pivoting ConditionalBox Plot Number To String Decompose Signal Rule-basedRow Splitter Table Writer Extract TableDimension Column Filter Variable toTable Row Table Writer Table Writer Column Filter PMML Writer ARIMA Predictor Line Plot Numeric Scorer Joiner Auto ARIMA Learner Numeric Outliers Line Plot Line color Line color Line color Line color

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Links

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: Maarit Laukkanen
Created at: 2020-06-09
On NodePit since: 2022-02-06
Last update: 2023-03-23
Created with KNIME version: v4.1.3
Tags: time seriesARIMAgranularitytrendseasonalityclassical decompositionseasonal plotlag plot