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  • API v1
    • Data
      • pytorch_forecasting.data.encoders.EncoderNormalizer
      • pytorch_forecasting.data.encoders.GroupNormalizer
      • pytorch_forecasting.data.encoders.MultiNormalizer
      • pytorch_forecasting.data.encoders.NaNLabelEncoder
      • pytorch_forecasting.data.encoders.TorchNormalizer
      • pytorch_forecasting.data.samplers.TimeSynchronizedBatchSampler
      • pytorch_forecasting.data.samplers.GroupedSampler
      • pytorch_forecasting.data.timeseries.TimeSeriesDataSet
    • Models
      • M Layer
        • pytorch_forecasting.models.deepar.DeepAR
        • pytorch_forecasting.models.mlp.DecoderMLP
        • pytorch_forecasting.models.nbeats.NBeats
        • pytorch_forecasting.models.nbeats.NBeatsKAN
        • pytorch_forecasting.models.nhits.NHiTS
        • pytorch_forecasting.models.rnn.RecurrentNetwork
        • pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer
        • pytorch_forecasting.models.tide.TiDEModel
        • pytorch_forecasting.models.timexer.TimeXer
        • pytorch_forecasting.models.xlstm.xLSTMTime
      • P Layer
        • pytorch_forecasting.models.deepar._deepar_pkg.DeepAR_pkg
        • pytorch_forecasting.models.mlp._decodermlp_pkg.DecoderMLP_pkg
        • pytorch_forecasting.models.nbeats._nbeats_pkg.NBeats_pkg
        • pytorch_forecasting.models.nbeats._nbeatskan_pkg.NBeatsKAN_pkg
        • pytorch_forecasting.models.nhits._nhits_pkg.NHiTS_pkg
        • pytorch_forecasting.models.rnn._rnn_pkg.RecurrentNetwork_pkg
        • pytorch_forecasting.models.temporal_fusion_transformer._tft_pkg.TemporalFusionTransformer_pkg
        • pytorch_forecasting.models.tide._tide_pkg.TiDEModel_pkg
    • Package
      • pytorch_forecasting.models.deepar._deepar_pkg.DeepAR_pkg
      • pytorch_forecasting.models.mlp._decodermlp_pkg.DecoderMLP_pkg
      • pytorch_forecasting.models.nbeats._nbeats_pkg.NBeats_pkg
      • pytorch_forecasting.models.nbeats._nbeatskan_pkg.NBeatsKAN_pkg
      • pytorch_forecasting.models.nhits._nhits_pkg.NHiTS_pkg
      • pytorch_forecasting.models.rnn._rnn_pkg.RecurrentNetwork_pkg
      • pytorch_forecasting.models.temporal_fusion_transformer._tft_pkg.TemporalFusionTransformer_pkg
      • pytorch_forecasting.models.tide._tide_pkg.TiDEModel_pkg
    • Metrics
      • pytorch_forecasting.metrics.quantile.QuantileLoss
      • pytorch_forecasting.metrics.point.CrossEntropy
      • pytorch_forecasting.metrics.point.PoissonLoss
      • pytorch_forecasting.metrics.point.SMAPE
      • pytorch_forecasting.metrics.point.MAPE
      • pytorch_forecasting.metrics.point.MAE
      • pytorch_forecasting.metrics.point.RMSE
      • pytorch_forecasting.metrics.point.MASE
      • pytorch_forecasting.metrics.point.TweedieLoss
      • pytorch_forecasting.metrics.distributions.NormalDistributionLoss
      • pytorch_forecasting.metrics.distributions.MultivariateNormalDistributionLoss
      • pytorch_forecasting.metrics.distributions.NegativeBinomialDistributionLoss
      • pytorch_forecasting.metrics.distributions.LogNormalDistributionLoss
      • pytorch_forecasting.metrics.distributions.BetaDistributionLoss
      • pytorch_forecasting.metrics.distributions.MQF2DistributionLoss
      • pytorch_forecasting.metrics.distributions.ImplicitQuantileNetworkDistributionLoss
    • Utils
      • utils
        • _classproperty
        • _coerce
        • _dependencies
        • _estimator_checks
        • _maint
        • _utils
    • Tutorials
      • Demand forecasting with the Temporal Fusion Transformer
      • Interpretable forecasting with N-Beats
      • How to use custom data and implement custom models and metrics
      • Autoregressive modelling with DeepAR and DeepVAR
      • Multivariate quantiles and long horizon forecasting with N-HiTS
  • API v2
    • Data
      • pytorch_forecasting.data.encoders.EncoderNormalizer
      • pytorch_forecasting.data.encoders.GroupNormalizer
      • pytorch_forecasting.data.encoders.MultiNormalizer
      • pytorch_forecasting.data.encoders.NaNLabelEncoder
      • pytorch_forecasting.data.encoders.TorchNormalizer
      • pytorch_forecasting.data.samplers.TimeSynchronizedBatchSampler
      • pytorch_forecasting.data.samplers.GroupedSampler
      • pytorch_forecasting.data.timeseries._timeseries_v2.TimeSeries
      • pytorch_forecasting.data.data_module._encoder_decoder_data_module.EncoderDecoderTimeSeriesDataModule
      • pytorch_forecasting.data.data_module._tslib_data_module.TslibDataModule
    • Models
      • M Layer
        • pytorch_forecasting.models.base._base_model_v2.BaseModel
        • pytorch_forecasting.models.base._tslib_base_model_v2.TslibBaseModel
        • pytorch_forecasting.models.temporal_fusion_transformer._tft_v2.TFT
        • pytorch_forecasting.models.dlinear._dlinear_v2.DLinear
        • pytorch_forecasting.models.samformer._samformer_v2.Samformer
        • pytorch_forecasting.models.tide._tide_dsipts._tide_v2.TIDE
        • pytorch_forecasting.models.timexer._timexer_v2.TimeXer
      • P Layer
        • pytorch_forecasting.models.temporal_fusion_transformer._tft_pkg_v2.TFT_pkg_v2
        • pytorch_forecasting.models.dlinear._dlinear_pkg_v2.DLinear_pkg_v2
        • pytorch_forecasting.models.samformer._samformer_v2_pkg.Samformer_pkg_v2
        • pytorch_forecasting.models.tide._tide_dsipts._tide_v2_pkg.TIDE_pkg_v2
        • pytorch_forecasting.models.timexer._timexer_pkg_v2.TimeXer_pkg_v2
    • Package
      • pytorch_forecasting.models.temporal_fusion_transformer._tft_pkg_v2.TFT_pkg_v2
      • pytorch_forecasting.models.dlinear._dlinear_pkg_v2.DLinear_pkg_v2
      • pytorch_forecasting.models.samformer._samformer_v2_pkg.Samformer_pkg_v2
      • pytorch_forecasting.models.tide._tide_dsipts._tide_v2_pkg.TIDE_pkg_v2
      • pytorch_forecasting.models.timexer._timexer_pkg_v2.TimeXer_pkg_v2
    • Metrics
      • pytorch_forecasting.metrics.quantile.QuantileLoss
      • pytorch_forecasting.metrics.point.CrossEntropy
      • pytorch_forecasting.metrics.point.PoissonLoss
      • pytorch_forecasting.metrics.point.SMAPE
      • pytorch_forecasting.metrics.point.MAPE
      • pytorch_forecasting.metrics.point.MAE
      • pytorch_forecasting.metrics.point.RMSE
      • pytorch_forecasting.metrics.point.MASE
      • pytorch_forecasting.metrics.point.TweedieLoss
      • pytorch_forecasting.metrics.distributions.NormalDistributionLoss
      • pytorch_forecasting.metrics.distributions.MultivariateNormalDistributionLoss
      • pytorch_forecasting.metrics.distributions.NegativeBinomialDistributionLoss
      • pytorch_forecasting.metrics.distributions.LogNormalDistributionLoss
      • pytorch_forecasting.metrics.distributions.BetaDistributionLoss
      • pytorch_forecasting.metrics.distributions.MQF2DistributionLoss
      • pytorch_forecasting.metrics.distributions.ImplicitQuantileNetworkDistributionLoss
    • Utils
      • utils
        • _classproperty
        • _coerce
        • _dependencies
        • _estimator_checks
        • _maint
        • _utils
    • Tutorials
      • Example Notebook for a basic vignette for pytorch-forecasting v2 Model Training and Inference
      • TSLib for v2 - Example notebook for full pipeline
  • Getting started
  • API
  • Utils in Pytorch Forecasting
  • utils
  • _dependencies
  • tests

tests#

Tests for dependency utilities.

Modules

test_safe_import

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_check_matplotlib

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test_safe_import
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