Many analysts struggle with preparing clean, reproducible time‑series panels for forecasting experiments: loading CSV files with correct date parsing, ensuring identifier columns are strings, setting a fixed random seed for synthetic data, checking GPU availability for model training, suppressing unnecessary warnings, and injecting known anomalies to evaluate detection methods. The following straightforward workflow tackles each of these issues in a few lines of code.
First, import essential libraries and silence warnings to keep output clean. Adjust pandas display options for wide dataframes and print library versions to verify the environment. Use a try‑except block to detect CUDA‑capable GPUs via PyTorch, storing the result in a boolean flag for later use.
Next, read the airline passengers dataset from a local path (replace “path/to/air_passengers.csv” with your file location) and parse the “ds” column as dates. Convert the “unique_id” column to string type to avoid type mismatches when concatenating panels later.
Create a synthetic series that mirrors the original timeline: instantiate a seeded random number generator, extract unique dates, and construct a DataFrame with a constant identifier, the date vector, and a target variable built from a linear trend, a yearly sinusoidal component, and Gaussian noise. Round the values to two decimals for readability.
Introduce three controlled anomalies by selecting specific index positions and multiplying their target values by a factor (2.2 in this example). Concatenate the original and synthetic dataframes, reset the index, and immediately inspect the resulting panel shape and basic statistics per identifier to confirm correct assembly.
Finally, define the forecast horizon (H) and frequency (FREQ) variables—here set to 12 months and month‑start alignment—ready for model training or evaluation scripts.
This compact recipe eliminates common setup friction, ensures reproducibility, and provides a clean testbed with labeled outliers for robust forecasting experiments.
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