How Zerve’s AI Agent helped me build a time series forecast

Zerve's AI Agent expedited the transformation of the Rossmann sales dataset from raw CSV files to a finely tuned forecast model without requiring manual coding. The process involved data cleaning, model selection, hyperparameter tuning, and visualization, all of which were automated to save time and enhance efficiency. The agent tested multiple forecasting models, including ETS, ARIMA, SARIMA, Prophet, LSTM, and XGBoost, ultimately selecting SARIMA as the best performer. The workflow allowed for human oversight and adjustments at every step, ensuring transparency and collaboration. This approach highlights how Zerve’s AI Agent can significantly accelerate time series forecasting by automating repetitive tasks, allowing users to focus on strategic decision-making and maintaining control over the process.

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