Impara Chronological Validation Splits

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When working with time series data, it is crucial to design validation strategies that respect the temporal order of your observations. Unlike general machine learning datasets, where random train/test splits are common, time series data has a natural sequence—future values should never influence the past. Using standard random splits can lead to data leakage, as information from the future may inadvertently be used to predict the past, resulting in overly optimistic validation scores and models that perform poorly in production.

In time series modeling, you must always ensure that validation data comes strictly after the training data in time. This chronological order mimics real-world forecasting, where you train on past data and predict the future. Ignoring this order can invalidate your evaluation and lead to unreliable models.


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import pandas as pd

# Create a sample time series DataFrame
data = {
    "date": pd.date_range("2023-01-01", periods=10, freq="D"),
    "value": [10, 12, 11, 13, 15, 16, 18, 17, 19, 20]
}
df = pd.DataFrame(data)

# Sort by date to ensure chronological order
df = df.sort_values("date")

# Define the split index (e.g., 70% train, 30% validation)
split_idx = int(len(df) * 0.7)

# Chronological split
train = df.iloc[:split_idx]
validation = df.iloc[split_idx:]

print("Train set:")
print(train)
print("\nValidation set:")
print(validation)

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Sezione 1. Capitolo 9

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Sezione 1. Capitolo 9