Predicting overall daily revenue requires handling intricate patterns. Cleaning the data, choosing the right models, tuning them, and producing visualizations can take days or even weeks.

I wanted to see how much faster I could get there using Zerve’s AI Agent. The goal was simple: take the Rossmann sales dataset from raw CSV files to a tuned forecast model with zero manual coding, while keeping full control over the process.

What followed was a workflow that stayed transparent, easy to adjust, and significantly faster than doing everything by hand.

Setting the Milestones

My plan was straightforward:

• Load and clean the data
• Build a Seasonal Naive baseline
• Test several forecasting models: ETS, ARIMA, SARIMA, Prophet, LSTM, and XGBoost
• Compare results side by side
• Select the best model using performance metrics
• Tune the model’s parameters
• Generate a final forecast

Step 1: Starting the Project

I gave the agent one short instruction: Build a time series forecast using Rossmann sales data.

It responded with a clear plan:

• Load train.csv and check the structure
• Clean and prepare the time series
• Build a basic forecast as a benchmark
• Compare predictions to the test set

I approved the plan and the agent began.

Step 2: Data Import and Cleaning

The agent imported the dataset, removed closed stores, dropped rows with missing sales, and confirmed the sales column was numeric. It also produced quick visualizations showing daily trends and sales distribution before moving to the next step.

Step 3: Model Building and Comparison

The first model was a Seasonal Naive forecast to set a baseline.

The agent then trained six models in parallel: ETS, ARIMA, SARIMA, Prophet, LSTM, and XGBoost.

It evaluated each model, logged performance metrics, and generated side-by-side forecast plots. SARIMA was the top performer.

Step 4: Hyperparameter Tuning

With SARIMA selected, the agent ran a grid search over p, d, q and seasonal parameters. I monitored the process and adjusted ranges when necessary.

Step 5: Final Forecast and Visualization

Once tuned, the SARIMA model:

• Generated forecasts for future dates
• Plotted predictions against actual values
• Produced residual diagnostics to validate the results

Manual Effort vs. Zerve Agent

The time savings were clear, but the bigger advantage was how collaborative the process felt. The agent presented each step so I could review and adjust the plan at any point.

What This Workflow Proves

Time series forecasting often requires going back and forth between cleaning, testing, and tuning. Zerve’s AI Agent keeps the process efficient, allowing you to move quickly while staying in control.

It not only keeps the human in the loop but can also look at its own output and decide the next steps, making the work faster, easier, and more flexible.

FAQs (Frequently Asked Questions)

What was the initial plan for building the time series forecast using Zerve's AI Agent?

The initial plan involved loading and cleaning the data, building a suitable forecasting model, tuning hyperparameters, and generating final forecasts with visualizations.

How did Zerve's AI Agent handle data import and cleaning for the forecasting project?

The agent imported the dataset, removed closed stores, dropped irrelevant columns, and prepared the data for modeling efficiently without manual intervention.

Which forecasting models were compared during the model building phase?

The first model was a Seasonal Naive forecast to establish a baseline, followed by SARIMA models which were selected for further tuning and comparison.

What role did hyperparameter tuning play in improving the SARIMA model?

Hyperparameter tuning involved running a grid search over parameters p, d, and q to optimize the SARIMA model's accuracy for better forecasting performance.

How did Zerve's AI Agent generate the final forecasts and visualizations?

Once tuned, the SARIMA model generated future sales forecasts which were then visualized to provide clear insights into expected sales trends across stores.

What advantages does using Zerve's AI Agent offer compared to manual time series forecasting efforts?

Beyond significant time savings, Zerve's AI Agent streamlines iterative processes like model selection and tuning, reducing back-and-forth adjustments typically required in time series forecasting workflows.