[WIP] [ENH] Add timeseries integration test for various models

tests/test_data/test_timeseries_integration.py

@@ -0,0 +1,95 @@
+import pickle
+import shutil
+
+import lightning.pytorch as pl
+from lightning.pytorch.callbacks import EarlyStopping
+from lightning.pytorch.loggers import TensorBoardLogger
+import numpy as np
+import pandas as pd
+import pytest
+
+from pytorch_forecasting.data.timeseries import TimeSeriesDataSet
+from pytorch_forecasting.metrics import MQF2DistributionLoss, QuantileLoss
+from pytorch_forecasting.metrics.distributions import (
+    ImplicitQuantileNetworkDistributionLoss,
+)
+from pytorch_forecasting.models import (
+    Baseline, 
+    DeepAR, 
+    DecoderMLP,
+    NBeats,
+    NHiTS,
+    GRU,
+    LSTM,
+    MultiEmbedding,
+    get_rnn,
+    RecurrentNetwork,
+    TemporalFusionTransformer,
+    TiDEModel,
+    TimeXer,
+)
+
+from pytorch_forecasting.utils._dependencies import _get_installed_packages
+
+
+MODELS = [
+    Baseline, 
+    DeepAR, 
+    DecoderMLP,
+    NBeats,
+    NHiTS,
+    GRU,
+    LSTM,
+    MultiEmbedding,
+    get_rnn,
+    RecurrentNetwork,
+    TemporalFusionTransformer,
+    TiDEModel,
+    TimeXer,
+]
+
+
+@pytest.mark.parametrize("model", MODELS)
+def test_integration(model):
+    n_timeseries = 10
+    time_points = 10
+    data = pd.DataFrame(
+        data={
+            "target": np.random.rand(time_points * n_timeseries),
+            "time_varying_known_real_1": np.random.rand(time_points * n_timeseries),
+            "time_idx": np.tile(np.arange(time_points), n_timeseries),
+            "group_id": np.repeat(np.arange(n_timeseries), time_points),
+        }
+    )
+    training_dataset = TimeSeriesDataSet(
+        data=data,
+        time_idx="time_idx",
+        target="target",
+        group_ids=["group_id"],
+        time_varying_unknown_reals=["target"],
+        time_varying_known_reals=(["time_varying_known_real_1"]),
+        max_prediction_length=max_prediction_length,
+        max_encoder_length=3,
+    )
+    training_data_loader = training_dataset.to_dataloader(train=True)
+    forecaster = model.from_dataset(training_dataset, log_val_interval=1)
+    trainer = pl.Trainer(
+        accelerator="cpu",
+        max_epochs=3,
+        min_epochs=2,
+        limit_train_batches=10,
+    )
+    trainer.fit(
+        forecaster,
+        train_dataloaders=training_data_loader,
+    )
+    validation_dataset = TimeSeriesDataSet.from_dataset(
+        training_dataset, data, stop_randomization=True, predict=True
+    )
+    validation_data_loader = validation_dataset.to_dataloader(train=False)
+    forecaster.predict(
+        validation_data_loader,
+        fast_dev_run=True,
+        return_index=True,
+        return_decoder_lengths=True,
+    )