You will get Time Series Forecasting for Sales, Demand, and Business Metrics
Project details
You will get a robust time series forecasting solution designed for real business data, where trends, seasonality, and structural changes matter.
I build forecasts using feature-based machine learning and classical time-series methods, combining lagged variables, rolling statistics, and calendar effects. This approach is flexible, interpretable, and easy to adapt as your business evolves.
The workflow includes proper backtesting, leakage-safe validation, and multiple forecasting scenarios so you can understand uncertainty and plan ahead with confidence. Deliverables focus on actionable forecasts, not just curves.
This project is ideal for demand planning, sales forecasting, capacity planning, and operational decision-making.
I build forecasts using feature-based machine learning and classical time-series methods, combining lagged variables, rolling statistics, and calendar effects. This approach is flexible, interpretable, and easy to adapt as your business evolves.
The workflow includes proper backtesting, leakage-safe validation, and multiple forecasting scenarios so you can understand uncertainty and plan ahead with confidence. Deliverables focus on actionable forecasts, not just curves.
This project is ideal for demand planning, sales forecasting, capacity planning, and operational decision-making.
Machine Learning Tools
GitHub Copilot, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, R, scikit-learn, SciPy, XGBoostWhat's included $250
These options are included with the project scope.
$250
- Delivery Time 9 days
- Number of Revisions 3
- Number of Model Variations 6
- Number of Scenarios 3
- Number of Graphs/Charts 9
- Model Validation/Testing
- Model Documentation
- Source Code
Optional add-ons
You can add these on the next page.
Fast 6 Days Delivery
+$150
Additional Scenario
(+ 2 Days)
+$75Frequently asked questions
About Pablo
Data Scientist
Montevideo, Uruguay - 7:31 am local time
I can help you with:
• Building and training classification, regression, or clustering models
• Designing full ML pipelines (data cleaning, feature engineering, evaluation)
• Computer Vision tasks such as detection, segmentation, or tracking
• Geospatial analysis using GeoPandas, QGIS, and satellite imagery
• NLP applications: text classification, summarization, and automation
• Data extraction, transformation, and analysis
Core technologies: Python, NumPy, Pandas, Scikit-Learn, PyTorch, TensorFlow, XGBoost, Optuna, GeoPandas, QGIS.
My approach is straightforward: understand the problem, design the most effective solution, and deliver clean, reproducible work.
Steps for completing your project
After purchasing the project, send requirements so Pablo can start the project.
Delivery time starts when Pablo receives requirements from you.
Pablo works on your project following the steps below.
Revisions may occur after the delivery date.
Data review & temporal validation
Review the series, validate frequency, handle missing values/outliers, and define leakage-safe train/test splits.
Feature engineering & baseline forecast
Create lag, rolling, and calendar features and establish a baseline forecast with proper metrics.