• Documentation
    • About ​ValidMind
    • Get Started
    • Guides
    • Support
    • Releases

    • Python Library
    • ValidMind Library

    • ValidMind Academy
    • Training Courses
  • Documentation
    • About ​ValidMind
    • Get Started
    • Guides
    • Support
    • Releases

    • Python Library
    • ValidMind Library

    • ValidMind Academy
    • Training Courses
  • Log In
    • Public Internet
    • ValidMind Platform · US1
    • ValidMind Platform · CA1

    • Private Link
    • Virtual Private ValidMind (VPV)

    • Which login should I use?
  1. Test descriptions
  2. Data Validation
  3. TimeSeriesDescription
  • ValidMind Library
  • Supported models

  • Quickstart
  • Quickstart for model documentation
  • Quickstart for model validation
  • Install and initialize ValidMind Library
  • Store model credentials in .env files

  • Model Development
  • 1 — Set up ValidMind Library
  • 2 — Start model development process
  • 3 — Integrate custom tests
  • 4 — Finalize testing & documentation

  • Model Validation
  • 1 — Set up ValidMind Library for validation
  • 2 — Start model validation process
  • 3 — Developing a challenger model
  • 4 — Finalize validation & reporting

  • Model Testing
  • Run tests & test suites
    • Add context to LLM-generated test descriptions
    • Intro to Assign Scores
    • Configure dataset features
    • Document multiple results for the same test
    • Explore test suites
    • Explore tests
    • Dataset Column Filters when Running Tests
    • Load dataset predictions
    • Log metrics over time
    • Run individual documentation sections
    • Run documentation tests with custom configurations
    • Run tests with multiple datasets
    • Intro to Unit Metrics
    • Understand and utilize RawData in ValidMind tests
    • Introduction to ValidMind Dataset and Model Objects
    • Run Tests
      • Run dataset based tests
      • Run comparison tests
  • Test descriptions
    • Data Validation
      • ACFandPACFPlot
      • ADF
      • AutoAR
      • AutoMA
      • AutoStationarity
      • BivariateScatterPlots
      • BoxPierce
      • ChiSquaredFeaturesTable
      • ClassImbalance
      • DatasetDescription
      • DatasetSplit
      • DescriptiveStatistics
      • DickeyFullerGLS
      • Duplicates
      • EngleGrangerCoint
      • FeatureTargetCorrelationPlot
      • HighCardinality
      • HighPearsonCorrelation
      • IQROutliersBarPlot
      • IQROutliersTable
      • IsolationForestOutliers
      • JarqueBera
      • KPSS
      • LaggedCorrelationHeatmap
      • LJungBox
      • MissingValues
      • MissingValuesBarPlot
      • MutualInformation
      • PearsonCorrelationMatrix
      • PhillipsPerronArch
      • ProtectedClassesCombination
      • ProtectedClassesDescription
      • ProtectedClassesDisparity
      • ProtectedClassesThresholdOptimizer
      • RollingStatsPlot
      • RunsTest
      • ScatterPlot
      • ScoreBandDefaultRates
      • SeasonalDecompose
      • ShapiroWilk
      • Skewness
      • SpreadPlot
      • TabularCategoricalBarPlots
      • TabularDateTimeHistograms
      • TabularDescriptionTables
      • TabularNumericalHistograms
      • TargetRateBarPlots
      • TimeSeriesDescription
      • TimeSeriesDescriptiveStatistics
      • TimeSeriesFrequency
      • TimeSeriesHistogram
      • TimeSeriesLinePlot
      • TimeSeriesMissingValues
      • TimeSeriesOutliers
      • TooManyZeroValues
      • UniqueRows
      • WOEBinPlots
      • WOEBinTable
      • ZivotAndrewsArch
      • Nlp
        • CommonWords
        • Hashtags
        • LanguageDetection
        • Mentions
        • PolarityAndSubjectivity
        • Punctuations
        • Sentiment
        • StopWords
        • TextDescription
        • Toxicity
    • Model Validation
      • BertScore
      • BleuScore
      • ClusterSizeDistribution
      • ContextualRecall
      • FeaturesAUC
      • MeteorScore
      • ModelMetadata
      • ModelPredictionResiduals
      • RegardScore
      • RegressionResidualsPlot
      • RougeScore
      • TimeSeriesPredictionsPlot
      • TimeSeriesPredictionWithCI
      • TimeSeriesR2SquareBySegments
      • TokenDisparity
      • ToxicityScore
      • Embeddings
        • ClusterDistribution
        • CosineSimilarityComparison
        • CosineSimilarityDistribution
        • CosineSimilarityHeatmap
        • DescriptiveAnalytics
        • EmbeddingsVisualization2D
        • EuclideanDistanceComparison
        • EuclideanDistanceHeatmap
        • PCAComponentsPairwisePlots
        • StabilityAnalysisKeyword
        • StabilityAnalysisRandomNoise
        • StabilityAnalysisSynonyms
        • StabilityAnalysisTranslation
        • TSNEComponentsPairwisePlots
      • Ragas
        • AnswerCorrectness
        • AspectCritic
        • ContextEntityRecall
        • ContextPrecision
        • ContextPrecisionWithoutReference
        • ContextRecall
        • Faithfulness
        • NoiseSensitivity
        • ResponseRelevancy
        • SemanticSimilarity
      • Sklearn
        • AdjustedMutualInformation
        • AdjustedRandIndex
        • CalibrationCurve
        • ClassifierPerformance
        • ClassifierThresholdOptimization
        • ClusterCosineSimilarity
        • ClusterPerformanceMetrics
        • CompletenessScore
        • ConfusionMatrix
        • FeatureImportance
        • FowlkesMallowsScore
        • HomogeneityScore
        • HyperParametersTuning
        • KMeansClustersOptimization
        • MinimumAccuracy
        • MinimumF1Score
        • MinimumROCAUCScore
        • ModelParameters
        • ModelsPerformanceComparison
        • OverfitDiagnosis
        • PermutationFeatureImportance
        • PopulationStabilityIndex
        • PrecisionRecallCurve
        • RegressionErrors
        • RegressionErrorsComparison
        • RegressionPerformance
        • RegressionR2Square
        • RegressionR2SquareComparison
        • RobustnessDiagnosis
        • ROCCurve
        • ScoreProbabilityAlignment
        • SHAPGlobalImportance
        • SilhouettePlot
        • TrainingTestDegradation
        • VMeasure
        • WeakspotsDiagnosis
      • Statsmodels
        • AutoARIMA
        • CumulativePredictionProbabilities
        • DurbinWatsonTest
        • GINITable
        • KolmogorovSmirnov
        • Lilliefors
        • PredictionProbabilitiesHistogram
        • RegressionCoeffs
        • RegressionFeatureSignificance
        • RegressionModelForecastPlot
        • RegressionModelForecastPlotLevels
        • RegressionModelSensitivityPlot
        • RegressionModelSummary
        • RegressionPermutationFeatureImportance
        • ScorecardHistogram
    • Ongoing Monitoring
      • CalibrationCurveDrift
      • ClassDiscriminationDrift
      • ClassificationAccuracyDrift
      • ClassImbalanceDrift
      • ConfusionMatrixDrift
      • CumulativePredictionProbabilitiesDrift
      • FeatureDrift
      • PredictionAcrossEachFeature
      • PredictionCorrelation
      • PredictionProbabilitiesHistogramDrift
      • PredictionQuantilesAcrossFeatures
      • ROCCurveDrift
      • ScoreBandsDrift
      • ScorecardHistogramDrift
      • TargetPredictionDistributionPlot
    • Prompt Validation
      • Bias
      • Clarity
      • Conciseness
      • Delimitation
      • NegativeInstruction
      • Robustness
      • Specificity
  • Test sandbox beta

  • Notebooks
  • Code samples
    • Capital Markets
      • Quickstart for knockout option pricing model documentation
      • Quickstart for Heston option pricing model using QuantLib
    • Code Explainer
      • Quickstart for model code documentation
    • Credit Risk
      • Document an application scorecard model
      • Document an application scorecard model
      • Document a credit risk model
      • Document an application scorecard model
    • Custom Tests
      • Implement custom tests
      • Integrate external test providers
    • Model Validation
      • Validate an application scorecard model
    • Nlp and Llm
      • Sentiment analysis of financial data using a large language model (LLM)
      • Summarization of financial data using a large language model (LLM)
      • Sentiment analysis of financial data using Hugging Face NLP models
      • Summarization of financial data using Hugging Face NLP models
      • Automate news summarization using LLMs
      • Prompt validation for large language models (LLMs)
      • RAG Model Benchmarking Demo
      • RAG Model Documentation Demo
    • Ongoing Monitoring
      • Ongoing Monitoring for Application Scorecard
      • Quickstart for ongoing monitoring of models with ValidMind
    • Regression
      • Document a California Housing Price Prediction regression model
    • Time Series
      • Document a time series forecasting model
      • Document a time series forecasting model

  • Reference
  • ValidMind Library Python API
  • ​ValidMind Public REST API

On this page

  • TimeSeriesDescription
    • Purpose
    • Test Mechanism
    • Signs of High Risk
    • Strengths
    • Limitations
  • Edit this page
  • Report an issue
  1. Test descriptions
  2. Data Validation
  3. TimeSeriesDescription

TimeSeriesDescription

Generates a detailed analysis for the provided time series dataset, summarizing key statistics to identify trends, patterns, and data quality issues.

Purpose

The TimeSeriesDescription function aims to analyze an individual time series by providing a summary of key statistics. This helps in understanding trends, patterns, and data quality issues within the time series.

Test Mechanism

The function extracts the time series data and provides a summary of key statistics. The dataset is expected to have a datetime index. The function checks this and raises an error if the index is not in datetime format. For each variable (column) in the dataset, appropriate statistics including start date, end date, frequency, number of missing values, count, min, and max values are calculated.

Signs of High Risk

  • If the index of the dataset is not in datetime format, it could lead to errors in time-series analysis.
  • Inconsistent or missing data within the dataset might affect the analysis of trends and patterns.

Strengths

  • Provides a comprehensive summary of key statistics for each variable, helping to identify data quality issues such as missing values.
  • Helps in understanding the distribution and range of the data by including min and max values.

Limitations

  • Assumes that the dataset is provided as a DataFrameDataset object with a .df attribute to access the pandas DataFrame.
  • Only analyzes datasets with a datetime index and will raise an error for other types of indices.
  • Does not handle large datasets efficiently; performance may degrade with very large datasets.
TargetRateBarPlots
TimeSeriesDescriptiveStatistics
  • ValidMind Logo
    ©
    Copyright 2025 ValidMind Inc.
    All Rights Reserved.
    Cookie preferences
    Legal
  • Get started
    • Model development
    • Model validation
    • Setup & admin
  • Guides
    • Access
    • Configuration
    • Model inventory
    • Model documentation
    • Model validation
    • Model workflows
    • Reporting
    • Monitoring
    • Attestation
  • Library
    • For developers
    • For validators
    • Code samples
    • API Reference
  • Training
    • Learning paths
    • Courses
    • Videos
  • Support
    • Troubleshooting
    • FAQ
    • Get help
  • Community
    • Slack
    • GitHub
    • Blog
  • Edit this page
  • Report an issue