From af94a866b7ea9604043c421a7db3edf17925e29d Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Thu, 22 May 2025 21:51:48 +0530
Subject: [PATCH 1/6] Basedeep forecaster added

---
 aeon/forecasting/deep_learning/__init__.py |   1 +
 aeon/forecasting/deep_learning/base.py     | 215 +++++++++++++++++++++
 2 files changed, 216 insertions(+)
 create mode 100644 aeon/forecasting/deep_learning/__init__.py
 create mode 100644 aeon/forecasting/deep_learning/base.py

diff --git a/aeon/forecasting/deep_learning/__init__.py b/aeon/forecasting/deep_learning/__init__.py
new file mode 100644
index 0000000000..42067031dc
--- /dev/null
+++ b/aeon/forecasting/deep_learning/__init__.py
@@ -0,0 +1 @@
+"""Initialization for aeon forecasting deep learning module."""
diff --git a/aeon/forecasting/deep_learning/base.py b/aeon/forecasting/deep_learning/base.py
new file mode 100644
index 0000000000..17eead9c49
--- /dev/null
+++ b/aeon/forecasting/deep_learning/base.py
@@ -0,0 +1,215 @@
+"""
+BaseDeepForecaster class.
+
+A simplified first base class for deep learning forecasting models.
+This class is a subclass of BaseForecaster and inherits its methods and attributes.
+It provides a base for deep learning models, including methods for training and
+predicting.
+
+"""
+
+from abc import abstractmethod
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+
+from aeon.forecasting.base import BaseForecaster
+
+
+class BaseDeepForecaster(BaseForecaster):
+    """Base class for deep learning forecasters in aeon.
+
+    Parameters
+    ----------
+    horizon : int, default=1
+        Forecasting horizon, the number of steps ahead to predict.
+    window : int, default=10
+        The window size for creating input sequences.
+    batch_size : int, default=32
+        Batch size for training the model.
+    epochs : int, default=100
+        Number of epochs to train the model.
+    verbose : int, default=0
+        Verbosity mode (0, 1, or 2).
+    optimizer : str or tf.keras.optimizers.Optimizer, default='adam'
+        Optimizer to use for training.
+    loss : str or tf.keras.losses.Loss, default='mse'
+        Loss function for training.
+    random_state : int, default=None
+        Seed for random number generators.
+    """
+
+    def __init__(
+        self,
+        horizon=1,
+        window=10,
+        batch_size=32,
+        epochs=100,
+        verbose=0,
+        optimizer="adam",
+        loss="mse",
+        random_state=None,
+    ):
+        self.horizon = horizon
+        self.window = window
+        self.batch_size = batch_size
+        self.epochs = epochs
+        self.verbose = verbose
+        self.optimizer = optimizer
+        self.loss = loss
+        self.random_state = random_state
+        self.model_ = None
+        super().__init__()
+
+    def _fit(self, y, X=None):
+        """Fit the forecaster to training data.
+
+        Parameters
+        ----------
+        y : np.ndarray or pd.Series
+            Target time series to which to fit the forecaster.
+        X : np.ndarray or pd.DataFrame, default=None
+            Exogenous variables.
+
+        Returns
+        -------
+        self : returns an instance of self
+        """
+        # Set random seed for reproducibility
+        if self.random_state is not None:
+            np.random.seed(self.random_state)
+            tf.random.set_seed(self.random_state)
+
+        # Convert input data to numpy array
+        y_inner = self._convert_input(y)
+
+        # Create sequences for training
+        X_train, y_train = self._create_sequences(y_inner)
+
+        # Build and compile the model
+        self.model_ = self._build_model(X_train.shape[1:])
+        self.model_.compile(optimizer=self.optimizer, loss=self.loss)
+
+        # Train the model
+        self.model_.fit(
+            X_train,
+            y_train,
+            batch_size=self.batch_size,
+            epochs=self.epochs,
+            verbose=self.verbose,
+        )
+
+        return self
+
+    def _predict(self, y=None, X=None):
+        """Make forecasts for y.
+
+        Parameters
+        ----------
+        y : np.ndarray or pd.Series, default=None
+            Series to predict from.
+        X : np.ndarray or pd.DataFrame, default=None
+            Exogenous variables.
+
+        Returns
+        -------
+        predictions : np.ndarray
+            Predicted values.
+        """
+        if y is None:
+            raise ValueError("y cannot be None for prediction")
+
+        # Convert input data to numpy array
+        y_inner = self._convert_input(y)
+
+        # Use the last window of data for prediction
+        last_window = y_inner[-self.window :].reshape(1, self.window, 1)
+
+        # Make prediction
+        prediction = self.model_.predict(last_window, verbose=0)
+
+        return prediction.flatten()
+
+    def _forecast(self, y, X=None):
+        """Forecast time series at future horizon.
+
+        Parameters
+        ----------
+        y : np.ndarray or pd.Series
+            Time series to forecast from.
+        X : np.ndarray or pd.DataFrame, default=None
+            Exogenous variables.
+
+        Returns
+        -------
+        forecasts : np.ndarray
+            Forecasted values.
+        """
+        # Fit the model
+        self._fit(y, X)
+
+        # Make prediction
+        return self._predict(y, X)
+
+    def _convert_input(self, y):
+        """Convert input data to numpy array.
+
+        Parameters
+        ----------
+        y : np.ndarray or pd.Series
+            Input time series.
+
+        Returns
+        -------
+        y_inner : np.ndarray
+            Converted numpy array.
+        """
+        if isinstance(y, pd.Series) or isinstance(y, pd.DataFrame):
+            y_inner = y.values
+        else:
+            y_inner = y
+
+        # Ensure 1D array
+        if len(y_inner.shape) > 1:
+            y_inner = y_inner.flatten()
+
+        return y_inner
+
+    def _create_sequences(self, data):
+        """Create input sequences and target values for training.
+
+        Parameters
+        ----------
+        data : np.ndarray
+            Time series data.
+
+        Returns
+        -------
+        X : np.ndarray
+            Input sequences.
+        y : np.ndarray
+            Target values.
+        """
+        X, y = [], []
+        for i in range(len(data) - self.window - self.horizon + 1):
+            X.append(data[i : (i + self.window)])
+            y.append(data[i + self.window : (i + self.window + self.horizon)])
+
+        return np.array(X).reshape(-1, self.window, 1), np.array(y)
+
+    @abstractmethod
+    def _build_model(self, input_shape):
+        """Build the deep learning model.
+
+        Parameters
+        ----------
+        input_shape : tuple
+            Shape of input data.
+
+        Returns
+        -------
+        model : tf.keras.Model
+            Compiled Keras model.
+        """
+        pass

From d2ee9ec5acda38de318ad22c1df0563b3f9d526f Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Mon, 26 May 2025 20:30:50 +0530
Subject: [PATCH 2/6] init for basedlf added

---
 aeon/forecasting/deep_learning/__init__.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/aeon/forecasting/deep_learning/__init__.py b/aeon/forecasting/deep_learning/__init__.py
index 42067031dc..c4b7a27030 100644
--- a/aeon/forecasting/deep_learning/__init__.py
+++ b/aeon/forecasting/deep_learning/__init__.py
@@ -1 +1,7 @@
 """Initialization for aeon forecasting deep learning module."""
+
+__all__ = [
+    "BaseDeepForecaster",
+]
+
+from aeon.forecasting.deep_learning.base import BaseDeepForecaster

From ab3030c0767fb773714c523a32963aa99c18c078 Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Sun, 15 Jun 2025 16:13:43 +0530
Subject: [PATCH 3/6] test file and axis added for basedeepforecaster

---
 aeon/forecasting/deep_learning/base.py        | 76 ++++++++++++++-----
 .../deep_learning/tests/__init__.py           |  1 +
 .../deep_learning/tests/test_base.py          | 62 +++++++++++++++
 3 files changed, 120 insertions(+), 19 deletions(-)
 create mode 100644 aeon/forecasting/deep_learning/tests/__init__.py
 create mode 100644 aeon/forecasting/deep_learning/tests/test_base.py

diff --git a/aeon/forecasting/deep_learning/base.py b/aeon/forecasting/deep_learning/base.py
index 17eead9c49..ebab0116bd 100644
--- a/aeon/forecasting/deep_learning/base.py
+++ b/aeon/forecasting/deep_learning/base.py
@@ -1,12 +1,13 @@
+"""Base class module for deep learning forecasters in aeon.
+
+This module defines the `BaseDeepForecaster` class, an abstract base class for
+deep learning-based forecasting models within the aeon toolkit.
 """
-BaseDeepForecaster class.
 
-A simplified first base class for deep learning forecasting models.
-This class is a subclass of BaseForecaster and inherits its methods and attributes.
-It provides a base for deep learning models, including methods for training and
-predicting.
+from __future__ import annotations
 
-"""
+__maintainer__ = []
+__all__ = ["BaseDeepForecaster"]
 
 from abc import abstractmethod
 
@@ -20,6 +21,9 @@
 class BaseDeepForecaster(BaseForecaster):
     """Base class for deep learning forecasters in aeon.
 
+    This class provides a foundation for deep learning-based forecasting models,
+    handling data preprocessing, model training, and prediction.
+
     Parameters
     ----------
     horizon : int, default=1
@@ -38,6 +42,9 @@ class BaseDeepForecaster(BaseForecaster):
         Loss function for training.
     random_state : int, default=None
         Seed for random number generators.
+    axis : int, default=0
+        Axis along which to apply the forecaster.
+        Default is 0 for univariate time series.
     """
 
     def __init__(
@@ -50,6 +57,7 @@ def __init__(
         optimizer="adam",
         loss="mse",
         random_state=None,
+        axis=0,
     ):
         self.horizon = horizon
         self.window = window
@@ -59,8 +67,11 @@ def __init__(
         self.optimizer = optimizer
         self.loss = loss
         self.random_state = random_state
+        self.axis = axis
         self.model_ = None
-        super().__init__()
+
+        # Pass horizon and axis to BaseForecaster
+        super().__init__(horizon=horizon, axis=axis)
 
     def _fit(self, y, X=None):
         """Fit the forecaster to training data.
@@ -74,7 +85,8 @@ def _fit(self, y, X=None):
 
         Returns
         -------
-        self : returns an instance of self
+        self : BaseDeepForecaster
+            Returns an instance of self.
         """
         # Set random seed for reproducibility
         if self.random_state is not None:
@@ -83,12 +95,21 @@ def _fit(self, y, X=None):
 
         # Convert input data to numpy array
         y_inner = self._convert_input(y)
+        if y_inner.shape[0] < self.window + self.horizon:
+            raise ValueError(
+                f"Data length ({y_inner.shape[0]}) is insufficient"
+                f"({self.window}) and horizon ({self.horizon})."
+            )
 
         # Create sequences for training
         X_train, y_train = self._create_sequences(y_inner)
 
+        if X_train.shape[0] == 0:
+            raise ValueError("No training sequences could be created.")
+
         # Build and compile the model
-        self.model_ = self._build_model(X_train.shape[1:])
+        input_shape = X_train.shape[1:]
+        self.model_ = self._build_model(input_shape)
         self.model_.compile(optimizer=self.optimizer, loss=self.loss)
 
         # Train the model
@@ -115,7 +136,7 @@ def _predict(self, y=None, X=None):
         Returns
         -------
         predictions : np.ndarray
-            Predicted values.
+            Predicted values for the specified horizon.
         """
         if y is None:
             raise ValueError("y cannot be None for prediction")
@@ -123,13 +144,26 @@ def _predict(self, y=None, X=None):
         # Convert input data to numpy array
         y_inner = self._convert_input(y)
 
+        if len(y_inner) < self.window:
+            raise ValueError(
+                f"Input data length ({len(y_inner)}) is less than the window size "
+                f"({self.window})."
+            )
+
         # Use the last window of data for prediction
         last_window = y_inner[-self.window :].reshape(1, self.window, 1)
 
         # Make prediction
-        prediction = self.model_.predict(last_window, verbose=0)
+        predictions = []
+        current_window = last_window
+        for _ in range(self.horizon):
+            pred = self.model_.predict(current_window, verbose=0)
+            predictions.append(pred[0, 0])
+            # Update the window with the latest prediction (autoregressive)
+            current_window = np.roll(current_window, -1, axis=1)
+            current_window[0, -1, 0] = pred[0, 0]
 
-        return prediction.flatten()
+        return np.array(predictions)
 
     def _forecast(self, y, X=None):
         """Forecast time series at future horizon.
@@ -144,13 +178,9 @@ def _forecast(self, y, X=None):
         Returns
         -------
         forecasts : np.ndarray
-            Forecasted values.
+            Forecasted values for the specified horizon.
         """
-        # Fit the model
-        self._fit(y, X)
-
-        # Make prediction
-        return self._predict(y, X)
+        return self._fit(y, X)._predict(y, X)
 
     def _convert_input(self, y):
         """Convert input data to numpy array.
@@ -191,12 +221,20 @@ def _create_sequences(self, data):
         y : np.ndarray
             Target values.
         """
+        if len(data) < self.window + self.horizon:
+            raise ValueError(
+                f"Data length ({len(data)}) is insufficient for window "
+                f"({self.window}) and horizon ({self.horizon})."
+            )
+
         X, y = [], []
         for i in range(len(data) - self.window - self.horizon + 1):
             X.append(data[i : (i + self.window)])
             y.append(data[i + self.window : (i + self.window + self.horizon)])
 
-        return np.array(X).reshape(-1, self.window, 1), np.array(y)
+        X = np.array(X).reshape(-1, self.window, 1)
+        y = np.array(y).reshape(-1, self.horizon)
+        return X, y
 
     @abstractmethod
     def _build_model(self, input_shape):
diff --git a/aeon/forecasting/deep_learning/tests/__init__.py b/aeon/forecasting/deep_learning/tests/__init__.py
new file mode 100644
index 0000000000..3dda9d25ea
--- /dev/null
+++ b/aeon/forecasting/deep_learning/tests/__init__.py
@@ -0,0 +1 @@
+"""Deep Learning Forecasting Tests File."""
diff --git a/aeon/forecasting/deep_learning/tests/test_base.py b/aeon/forecasting/deep_learning/tests/test_base.py
new file mode 100644
index 0000000000..05536f98c5
--- /dev/null
+++ b/aeon/forecasting/deep_learning/tests/test_base.py
@@ -0,0 +1,62 @@
+"""Test for BaseDeepForecaster class in aeon."""
+
+import numpy as np
+import pytest
+
+from aeon.forecasting.deep_learning import BaseDeepForecaster
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+class SimpleDeepForecaster(BaseDeepForecaster):
+    """A simple concrete implementation of BaseDeepForecaster for testing."""
+
+    def _build_model(self, input_shape):
+        import tensorflow as tf
+
+        model = tf.keras.Sequential(
+            [
+                tf.keras.layers.Flatten(input_shape=input_shape),
+                tf.keras.layers.Dense(10, activation="relu"),
+                tf.keras.layers.Dense(self.horizon),
+            ]
+        )
+        return model
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies("tensorflow", severity="none"),
+    reason="skip test if required soft dependency not available",
+)
+def test_base_deep_forecaster_fit_predict():
+    """Test fitting and predicting with BaseDeepForecaster implementation."""
+    # Generate synthetic data
+    np.random.seed(42)
+    data = np.random.randn(50)
+
+    # Initialize forecaster
+    forecaster = SimpleDeepForecaster(horizon=2, window=5, epochs=1, verbose=0)
+
+    # Fit the model
+    forecaster.fit(data)
+
+    # Predict
+    predictions = forecaster.predict(data)
+
+    # Validate output shape
+    assert (
+        len(predictions) == 2
+    ), f"Expected predictions of length 2, got {len(predictions)}"
+    assert isinstance(predictions, np.ndarray), "Predictions should be a numpy array"
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies("tensorflow", severity="none"),
+    reason="skip test if required soft dependency not available",
+)
+def test_base_deep_forecaster_insufficient_data():
+    """Test error handling for insufficient data."""
+    data = np.random.randn(5)
+    forecaster = SimpleDeepForecaster(horizon=2, window=5, epochs=1, verbose=0)
+
+    with pytest.raises(ValueError, match="Data length.*insufficient"):
+        forecaster.fit(data)

From 1f202db1cae45834503986ffb37859599b29759a Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Sun, 15 Jun 2025 16:38:39 +0530
Subject: [PATCH 4/6] test locally

---
 .github/workflows/pr_pytest.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml
index cf1baee900..9c70b16b4c 100644
--- a/.github/workflows/pr_pytest.yml
+++ b/.github/workflows/pr_pytest.yml
@@ -3,7 +3,7 @@ name: PR pytest
 on:
   push:
     branches:
-      - main
+      - basedlf
   pull_request:
     paths:
       - "aeon/**"

From 14eb41fa83a5799d0fa8608ffd516f1766da7a1c Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Sun, 15 Jun 2025 17:19:27 +0530
Subject: [PATCH 5/6] dlf corrected

---
 .github/workflows/pr_pytest.yml                   | 2 +-
 aeon/forecasting/deep_learning/tests/test_base.py | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/pr_pytest.yml b/.github/workflows/pr_pytest.yml
index 9c70b16b4c..cf1baee900 100644
--- a/.github/workflows/pr_pytest.yml
+++ b/.github/workflows/pr_pytest.yml
@@ -3,7 +3,7 @@ name: PR pytest
 on:
   push:
     branches:
-      - basedlf
+      - main
   pull_request:
     paths:
       - "aeon/**"
diff --git a/aeon/forecasting/deep_learning/tests/test_base.py b/aeon/forecasting/deep_learning/tests/test_base.py
index 05536f98c5..1eae0969e1 100644
--- a/aeon/forecasting/deep_learning/tests/test_base.py
+++ b/aeon/forecasting/deep_learning/tests/test_base.py
@@ -10,6 +10,9 @@
 class SimpleDeepForecaster(BaseDeepForecaster):
     """A simple concrete implementation of BaseDeepForecaster for testing."""
 
+    def __init__(self, horizon=1, window=5, epochs=1, verbose=0):
+        super().__init__(horizon=horizon, window=window, epochs=epochs, verbose=verbose)
+
     def _build_model(self, input_shape):
         import tensorflow as tf
 

From d1a2aab72097dd38658fa0a10f572005c5b70aaa Mon Sep 17 00:00:00 2001
From: lucifer4073 <lucifer4073@gmail.com>
Date: Sun, 22 Jun 2025 12:27:15 +0530
Subject: [PATCH 6/6] tf soft dep added

---
 aeon/forecasting/deep_learning/base.py            | 3 ++-
 aeon/forecasting/deep_learning/tests/test_base.py | 4 ++++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/aeon/forecasting/deep_learning/base.py b/aeon/forecasting/deep_learning/base.py
index ebab0116bd..ba33331fc7 100644
--- a/aeon/forecasting/deep_learning/base.py
+++ b/aeon/forecasting/deep_learning/base.py
@@ -13,7 +13,6 @@
 
 import numpy as np
 import pandas as pd
-import tensorflow as tf
 
 from aeon.forecasting.base import BaseForecaster
 
@@ -88,6 +87,8 @@ def _fit(self, y, X=None):
         self : BaseDeepForecaster
             Returns an instance of self.
         """
+        import tensorflow as tf
+
         # Set random seed for reproducibility
         if self.random_state is not None:
             np.random.seed(self.random_state)
diff --git a/aeon/forecasting/deep_learning/tests/test_base.py b/aeon/forecasting/deep_learning/tests/test_base.py
index 1eae0969e1..270a60225e 100644
--- a/aeon/forecasting/deep_learning/tests/test_base.py
+++ b/aeon/forecasting/deep_learning/tests/test_base.py
@@ -7,6 +7,10 @@
 from aeon.utils.validation._dependencies import _check_soft_dependencies
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies("tensorflow", severity="none"),
+    reason="skip test if required soft dependency not available",
+)
 class SimpleDeepForecaster(BaseDeepForecaster):
     """A simple concrete implementation of BaseDeepForecaster for testing."""