Forecasting & Predictive Modeling
The difference between guessing and forecasting is a model that captures your data's patterns and tells you what to expect. I build production-grade forecast and risk models tuned to your business: revenue, demand, churn, attrition.
The Problem
Spreadsheet forecasts become outdated the moment they're made. Forecasts built by consultants live in a deck, not in your daily decision-making. You need something you trust, update weekly, and can act on.
What You Get
A production model updated automatically from your data source
Weekly/monthly forecasts with confidence intervals so you know the range
Explainability: which factors drive the forecast, not a black box
Tuning to your business: seasonal patterns, trend breaks, anomalies
Integration into your BI layer so forecasts live next to actuals
Relevant Work
Bot Traffic Scoring Model for E-Commerce Reporting
Built a hybrid bot traffic scoring model to protect KPI accuracy in e-commerce business performance reporting — catching sophisticated bots that evaded existing third-party filtering.
Demand Forecasting and Assortment Optimization — Apparel
Built Python-based demand forecasting and assortment optimization models for a global retailer, paired with executive dashboards.
Questions
How much historical data do I need?▾
Time-series models need at least 2 years of clean data to capture seasonality. More is better. With less, we can use external data or simpler approaches.
What happens when business changes (product launch, crisis)?▾
Good models degrade gracefully but don't auto-correct for regime changes. I build in manual override layers and retrain quarterly to adapt to new patterns.
Do you build neural nets or deep learning?▾
Only when necessary. 90% of forecasting problems are solved better with classical time-series (ARIMA, Prophet) or gradient boosting. I choose the simplest model that works.
Ready to get started?
Let's talk about how this service fits your needs. Book a call or send a message.
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