TSCV: Prep tutorial

docs/guides/07_timeseries_crossvalidation.qmd

@@ -0,0 +1,134 @@
+---
+title: "Time Series Cross Validation"
+jupyter: python3
+toc: true
+toc-depth: 3
+number-sections: true
+number-depth: 2
+---
+
+## Time-Based Cross-Validation Using `TimeSeriesCV` and `TimeSeriesCVSplitter`
+
+In this tutorial, you'll learn how to use the `TimeSeriesCV` and `TimeSeriesCVSplitter` classes from `pytimetk` for time series cross-validation, using the walmart_sales_df dataset as an example. We'll start with exploring the data and move on to creating and visualizing time-based cross-validation splits.
+
+### Step 1: Load and Explore the Data
+
+First, let's load the Walmart sales dataset and explore its structure:
+
+```{python}
+# libraries
+import pytimetk as tk
+import pandas as pd
+import numpy as np
+
+# Import Data
+walmart_sales_df = tk.load_dataset('walmart_sales_weekly')
+
+walmart_sales_df['Date'] = pd.to_datetime(walmart_sales_df['Date'])
+
+walmart_sales_df = walmart_sales_df[['id', 'Date', 'Weekly_Sales']]
+
+walmart_sales_df.glimpse()
+```
+
+### Step 2: Visualize the Time Series Data
+
+We can visualize the weekly sales data for different store IDs using the `plot_timeseries` method from `pytimetk`:
+
+```{python}
+walmart_sales_df \
+    .groupby('id') \
+    .plot_timeseries(
+        "Date", "Weekly_Sales",
+        plotly_dropdown = True,
+    )
+```
+
+This will generate an interactive time series plot, allowing you to explore sales data for different stores using a dropdown.
+
+### Step 3: Set Up `TimeSeriesCV` for Cross-Validation
+
+Now, let's set up a time-based cross-validation scheme using `TimeSeriesCV`:
+
+```{python}
+from pytimetk.crossvalidation import TimeSeriesCV
+
+# Define parameters for TimeSeriesCV
+tscv = TimeSeriesCV(
+    frequency="weeks",
+    train_size=52,          # Use 52 weeks for training
+    forecast_horizon=12,    # Forecast 12 weeks ahead
+    gap=0,                  # No gap between training and forecast sets
+    stride=4,               # Move forward by 4 weeks after each split
+    window="rolling",       # Use a rolling window
+    mode="backward"         # Generate splits from end to start
+)
+
+# Glimpse the cross-validation splits
+tscv.glimpse(
+    walmart_sales_df['Weekly_Sales'], 
+    time_series=walmart_sales_df['Date']
+)
+```
+
+The `glimpse` method provides a summary of each cross-validation fold, including the start and end dates of the training and forecast periods.
+
+
+### Step 4: Plot the Cross-Validation Splits
+
+You can visualize how the data is split for training and testing:
+
+```{python}
+# Plot the cross-validation splits
+tscv.plot(
+    walmart_sales_df['Weekly_Sales'], 
+    time_series=walmart_sales_df['Date']
+)
+```
+
+This plot will show each fold, illustrating which weeks are used for training and which weeks are used for forecasting.
+
+### Step 5: Using `TimeSeriesCVSplitter` for Model Evaluation
+
+If you want to use the cross-validation scheme with scikit-learn's model evaluation methods, you can use `TimeSeriesCVSplitter`:
+
+
+```{python}
+from pytimetk.crossvalidation import TimeSeriesCVSplitter
+from sklearn.linear_model import Ridge
+from sklearn.model_selection import cross_val_score
+
+# Set up TimeSeriesCVSplitter
+cv_splitter = TimeSeriesCVSplitter(
+    time_series=walmart_sales_df['Date'],
+    frequency="weeks",
+    train_size=52,
+    forecast_horizon=4,
+    gap=0,
+    stride=4,
+    window="rolling",
+    mode="backward",
+    split_limit = 5
+)
+
+# Prepare data for modeling
+
+# Extract time series features from the 'Date' column
+X_time_features = tk.get_timeseries_signature(walmart_sales_df['Date']).drop('Date', axis=1)
+
+# Dummy encode the 'id' column
+X_id_dummies = pd.get_dummies(walmart_sales_df['id'], prefix='store')
+
+# Combine the time series features and dummy encoded features
+X = pd.concat([X_time_features, X_id_dummies], axis=1)
+
+# Target
+y = walmart_sales_df['Weekly_Sales'].values    
+
+# Fit and evaluate a model using cross-validation
+model = Ridge()
+scores = cross_val_score(model, X, y, cv=cv_splitter)
+
+# Print cross-validation scores
+print("Cross-Validation Scores:", scores)
+```

src/pytimetk/crossvalidation/time_series_cv.py

@@ -401,19 +401,6 @@ def plot(
                 showlegend=False,
             ))
 
-            # Optionally, add text annotations for each fold
-            train_midpoint = ts_date + (te_date - ts_date) / 2
-
-            fig.add_trace(go.Scatter(
-                x=[train_midpoint],
-                y=[fold],
-                text=[f"Fold {fold}"],
-                mode="text",
-                showlegend=False,
-                textposition="middle center",
-                hoverinfo='skip',
-            ))
-
         # Update layout
         fig.update_layout(
             title=title,