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fusionlab-learn 0.3.1 documentation
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Documentation

  • Installation
  • Quickstart
  • Motivation
  • User Guide
    • Introduction
    • Forecasting Models
      • Base Attentive Model Architecture
      • Hybrid Models
        • HALNet (Hybrid Attentive LSTM Network)
        • Hybrid Transformer Models: XTFT & SuperXTFT
      • Physics-Informed Neural Networks (PINNs)
        • Hybrid Physics-Data Models: PiHALNet & PIHALNet
        • TransFlowSubsNet: A Physics-Informed Hybrid Forecasting Model
        • Physics-Informed Transient Groundwater Flow (PiTGWFlow)
        • GeoPrior has moved
      • Transformer-Based Models
        • Pure Transformer Models
        • Temporal Fusion Transformer (TFT) and Variants
    • Core Model Components
    • Loss Functions
    • Physical Parameter Descriptors
    • Datasets
    • Data Preparation Workflow
    • Utilities for fusionlab-learn
      • Data Manipulation Utilities
      • Time Series Utilities
      • Geospatial & Time Series Data Utilities
      • Spatial Data Utilities
      • Neural Network Utilities
      • PINN Data Utilities
      • Forecast Data Formatting Utilities
    • Hyperparameter Tuning
      • Hyperparameter Tuning with HydroTuner
      • HydroTuner: Usage Examples
      • Tuning HALNet with the XTFTTuner
      • TFT Forecast Tuner Guide
      • TFT & XTFT Tuning Examples
      • XTFT Tuning
      • Class-Based Tuner Guide
      • Example: Tuning PIHALNet with PIHALTuner
      • Hyperparameter Tuning (Legacy PiHALTuner)
    • Model Evaluation and Visualization
      • Evaluating and Visualizing Forecasts
      • Metrics for Forecasting Evaluation
    • Anomaly Detection
    • Examples Gallery
      • Forecasting Examples
        • Basic Point Forecasting with Flexible TemporalFusionTransformer
        • Quantile Forecasting with TFT Variants
        • Point Forecasting with Stricter TFT (Required Inputs)
        • Advanced Forecasting with XTFT
        • XTFT Forecasting with Anomaly Detection
      • Anomaly Detection Examples
      • Plotting & Visualization Gallery
        • Plotting Utilities
        • Forecast Visualization Utilities
        • Visualizing Forecasts with K-Diagram
      • Using Command-Line Tools
    • Case Histories
    • Hands-On Exercises
      • Exercise: Basic Point Forecasting with Flexible TFT
      • Exercise: Data Preparation Workflow for Case History Data
      • Exercise: Quantile Forecasting with TFT Variants
      • Exercise: Advanced Forecasting with BaseAttentive
      • Exercise: Forecasting with HALNet (All Inputs Required)
      • Exercise: Forecasting with a Pure Transformer
      • Exercise: Forecasting with Stricter TFT (All Inputs Required)
      • Exercise: Advanced Quantile Forecasting with XTFT
      • Exercise: Advanced Forecasting with SuperXTFT
      • Exercise: Hybrid Forecasting with PIHALNet
      • Exercise: Hybrid Forecasting with TransFlowSubsNet
      • Exercise: Physics-Informed Forecasting with PIHALNet & TransFlowSubsNet
      • Exercise: Solving a Forward Problem with PiTGWFlow
      • Exercise: A Basic PIHALNet Forecasting Workflow
      • Exercise: Hyperparameter Tuning with HydroTuner
      • Exercise: Anomaly Detection
    • Command-Line Interface (CLI)
    • Subsidence PINN: Mini Forecaster Guide
    • Applications (GUI)
      • GeoPrior v3 GUI
        • Overview
        • Installation and startup
        • Quickstart (end-to-end)
        • Tabs and workflow
          • Data tab
          • Experiment Setup tab
          • Preprocess tab (Stage-1)
          • Train tab
          • Tune tab
          • Inference tab
          • Transferability tab
          • Map tab
          • Results tab
          • Tools tab
        • GUI components
          • Navigation and state
          • Presets & profiles
          • Log panel
          • Progress and threads
          • File browser and exports
          • Map analytics panel
        • Reference
          • Output folders and file layout
          • Configuration key reference
          • CLI equivalence and scripts
          • Troubleshooting
          • FAQ
          • Changelog
  • API Reference
    • fusionlab.nn.transformers.TimeSeriesTransformer
    • fusionlab.nn.transformers.TemporalFusionTransformer
    • fusionlab.nn.transformers.TFT
    • fusionlab.nn.transformers.DummyTFT
    • fusionlab.nn.models.BaseAttentive
    • fusionlab.nn.models.HALNet
    • fusionlab.nn.models.XTFT
    • fusionlab.nn.models.SuperXTFT
    • fusionlab.nn.pinn.TransFlowSubsNet
    • fusionlab.nn.pinn.models.PIHALNet
    • fusionlab.nn.pinn.PiHALNet
    • fusionlab.nn.pinn.PiTGWFlow
    • fusionlab.nn.components.GatedResidualNetwork
    • fusionlab.nn.components.VariableSelectionNetwork
    • fusionlab.nn.components.PositionalEncoding
    • fusionlab.nn.components.StaticEnrichmentLayer
    • fusionlab.nn.components.LearnedNormalization
    • fusionlab.nn.components.MultiScaleLSTM
    • fusionlab.nn.components.DynamicTimeWindow
    • fusionlab.nn.components.aggregate_multiscale
    • fusionlab.nn.components.aggregate_multiscale_on_3d
    • fusionlab.nn.components.aggregate_time_window_output
    • fusionlab.nn.components.create_causal_mask
    • fusionlab.nn.components.TemporalAttentionLayer
    • fusionlab.nn.components.CrossAttention
    • fusionlab.nn.components.HierarchicalAttention
    • fusionlab.nn.components.MemoryAugmentedAttention
    • fusionlab.nn.components.MultiResolutionAttentionFusion
    • fusionlab.nn.components.ExplainableAttention
    • fusionlab.nn.components.MultiModalEmbedding
    • fusionlab.nn.components.MultiDecoder
    • fusionlab.nn.components.QuantileDistributionModeling
    • fusionlab.nn.components.AdaptiveQuantileLoss
    • fusionlab.nn.components.AnomalyLoss
    • fusionlab.nn.components.MultiObjectiveLoss
    • fusionlab.nn.losses.combined_quantile_loss
    • fusionlab.nn.losses.prediction_based_loss
    • fusionlab.nn.losses.combined_total_loss
    • fusionlab.nn.losses.objective_loss
    • fusionlab.nn.losses.quantile_loss
    • fusionlab.nn.losses.quantile_loss_multi
    • fusionlab.nn.losses.anomaly_loss
    • fusionlab.nn.anomaly_detection.LSTMAutoencoderAnomaly
    • fusionlab.nn.anomaly_detection.SequenceAnomalyScoreLayer
    • fusionlab.nn.anomaly_detection.PredictionErrorAnomalyScore
    • fusionlab.nn.forecast_tuner.HydroTuner
    • fusionlab.nn.forecast_tuner.HALTuner
    • fusionlab.nn.forecast_tuner.XTFTTuner
    • fusionlab.nn.forecast_tuner.TFTTuner
    • fusionlab.nn.forecast_tuner.PiHALTuner
    • fusionlab.nn.forecast_tuner.xtft_tuner
    • fusionlab.nn.forecast_tuner.tft_tuner
    • fusionlab.nn.utils.create_sequences
    • fusionlab.nn.utils.split_static_dynamic
    • fusionlab.nn.utils.reshape_xtft_data
    • fusionlab.nn.utils.compute_forecast_horizon
    • fusionlab.nn.utils.prepare_spatial_future_data
    • fusionlab.nn.utils.compute_anomaly_scores
    • fusionlab.nn.utils.generate_forecast
    • fusionlab.nn.utils.generate_forecast_with
    • fusionlab.nn.utils.forecast_single_step
    • fusionlab.nn.utils.forecast_multi_step
    • fusionlab.nn.utils.step_to_long
    • fusionlab.nn.utils.format_predictions
    • fusionlab.nn.utils.format_predictions_to_dataframe
    • fusionlab.nn.utils.prepare_model_inputs
    • fusionlab.nn.utils.format_pihalnet_predictions
    • fusionlab.nn.utils.prepare_pinn_data_sequences
    • fusionlab.nn.utils.format_pinn_predictions
    • fusionlab.params.LearnableK
    • fusionlab.params.LearnableSs
    • fusionlab.params.LearnableQ
    • fusionlab.params.LearnableC
    • fusionlab.params.FixedC
    • fusionlab.params.DisabledC
    • fusionlab.params.resolve_physical_param
    • fusionlab.metrics.coverage_score
    • fusionlab.metrics.continuous_ranked_probability_score
    • fusionlab.metrics.mean_interval_width_score
    • fusionlab.metrics.prediction_stability_score
    • fusionlab.metrics.quantile_calibration_error
    • fusionlab.metrics.theils_u_score
    • fusionlab.metrics.time_weighted_accuracy_score
    • fusionlab.metrics.time_weighted_interval_score
    • fusionlab.metrics.time_weighted_mean_absolute_error
    • fusionlab.metrics.weighted_interval_score
    • fusionlab.plot.evaluation.plot_coverage
    • fusionlab.plot.evaluation.plot_crps
    • fusionlab.plot.evaluation.plot_forecast_comparison
    • fusionlab.plot.evaluation.plot_mean_interval_width
    • fusionlab.plot.evaluation.plot_metric_over_horizon
    • fusionlab.plot.evaluation.plot_metric_radar
    • fusionlab.plot.evaluation.plot_prediction_stability
    • fusionlab.plot.evaluation.plot_quantile_calibration
    • fusionlab.plot.evaluation.plot_theils_u_score
    • fusionlab.plot.evaluation.plot_time_weighted_metric
    • fusionlab.plot.evaluation.plot_weighted_interval_score
    • fusionlab.plot.forecast.forecast_view
    • fusionlab.plot.forecast.plot_forecasts
    • fusionlab.plot.forecast.plot_forecast_by_step
    • fusionlab.plot.forecast.visualize_forecasts
    • fusionlab.utils.nan_ops
    • fusionlab.utils.widen_temporal_columns
    • fusionlab.utils.pivot_forecast_dataframe
    • fusionlab.utils.create_spatial_clusters
    • fusionlab.utils.spatial_sampling
    • fusionlab.utils.augment_series_features
    • fusionlab.utils.generate_dummy_pinn_data
    • fusionlab.utils.augment_spatiotemporal_data
    • fusionlab.utils.mask_by_reference
    • fusionlab.utils.fetch_joblib_data
    • fusionlab.utils.save_job
    • fusionlab.utils.ts_utils.ts_validator
    • fusionlab.utils.ts_utils.to_dt
    • fusionlab.utils.ts_utils.filter_by_period
    • fusionlab.utils.ts_utils.ts_engineering
    • fusionlab.utils.ts_utils.create_lag_features
    • fusionlab.utils.ts_utils.trend_analysis
    • fusionlab.utils.ts_utils.trend_ops
    • fusionlab.utils.ts_utils.decompose_ts
    • fusionlab.utils.ts_utils.get_decomposition_method
    • fusionlab.utils.ts_utils.infer_decomposition_method
    • fusionlab.utils.ts_utils.ts_corr_analysis
    • fusionlab.utils.ts_utils.transform_stationarity
    • fusionlab.utils.ts_utils.ts_split
    • fusionlab.utils.ts_utils.ts_outlier_detector
    • fusionlab.utils.ts_utils.select_and_reduce_features
    • fusionlab.datasets.fetch_zhongshan_data
    • fusionlab.datasets.fetch_nansha_data
    • fusionlab.datasets.load_processed_subsidence_data
    • fusionlab.datasets.load_subsidence_pinn_data
    • fusionlab.datasets.make_multi_feature_time_series
    • fusionlab.datasets.make_quantile_prediction_data
    • fusionlab.datasets.make_anomaly_data
    • fusionlab.datasets.make_trend_seasonal_data
    • fusionlab.datasets.make_multivariate_target_data
  • Contributing
  • Code of Conduct
  • How to Cite
  • Release Notes
    • Version 0.3.1
    • Version 0.3.0
    • Version 0.2.3
    • Version 0.2.2
    • Version 0.2.1
    • Version 0.2.0
    • Version 0.1.1
    • Version 0.1.0
  • Glossary
  • License
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Navigation and state¶

This section explains:

  • the tab strip and how workflow state flows from left to right,

  • how the application remembers per-tab UI state,

  • how the store acts as the single source of truth.

Add a screenshot of the top navigation and status chips.

Navigation and status area.

Navigation strip, status chips, and global actions.¶

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