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I will build a time series forecasting model for any business
AI and Data Science Expert
AI Lead with 7+ years building production AI systems for governments, banks, and telecoms. Currently deploying AI agents for Qatar's national smart city platform (TASMU), processing 1,000+ weekly quer...
About this Gig
Bad forecasts are expensive. Overstocked shelves, understaffed shifts, missed revenue targets, and idle cash, all caused by models that looked good on paper but failed in production.
I build forecasting systems that actually work. At Faysal Bank, my cash demand forecasting model reduced shortages by 90% and delivered PKR 124M in annual savings by combining rigorous feature engineering, ensemble methods, and continuous error analysis rather than relying on a single off-the-shelf model.
Whether you need to forecast sales, inventory, cash flow, energy consumption, or staffing demand, I'll run a structured model comparison, tune for your specific error metric, and deliver production-ready code with a full accuracy report.
No black boxes. Every forecast comes with explainability, confidence intervals, and honest error analysis.
Programming language:
Python
•
SQL
•
NoSQL
•
MLflow
•
Amazon SageMaker
Frameworks:
Scikit-learn
•
Google ML Kit
•
Keras
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PyTorch
•
Panda
FAQ
How much historical data do I need?
As a rule of thumb, at least 2 full seasonal cycles of data (e.g. 2 years for annual seasonality). Less is workable. I'll advise on which models are appropriate for your data volume and flag confidence limits honestly.
Which model will you use; and why not just use Prophet?
Model choice depends on your data. Prophet works well for clean trend + seasonality patterns. I run a structured comparison and let the evaluation metrics decide, not assumptions.
How do you measure forecast accuracy?
Every delivery includes a full error analysis: MAE, RMSE, MAPE, and bias breakdown. I use time-aware cross-validation (walk-forward) so accuracy numbers reflect real-world conditions, not leaky in-sample performance.
Can the model update automatically as new data comes in?
Yes! The Premium tier includes a real-time inference pipeline with automated retraining triggers and MLflow tracking so model drift is caught early.
Can you forecast multiple products, locations, or series at once?
Yes. Multi-series (hierarchical) forecasting is included in the Premium tier. For very large scale (1,000+ series), contact me for a custom quote.
Do you sign NDAs?
Yes, happy to sign before kickoff. Financial and operational data is treated with strict confidentiality.
