Operational Time Series Forecasting
Budget: $250 – $750 USD
I have several months of raw operational data coming in at regular intervals and I need a clear, reliable way to turn those numbers into forward-looking forecasts. My main aim is to predict future outcomes—downtime, throughput, and other key performance indicators—so we can plan resources and spot issues before they bite.
You will receive the cleaned CSV extracts along with a brief on the variables that matter most. I’m leaning toward classic and modern time-series techniques—ARIMA, SARIMA, Prophet, or any other method you can justify—so long as the model you build is defensible and produces error metrics we can trust. Python (pandas, NumPy, scikit-learn) or R (forecast, tidyverse) are both fine; choose whichever lets you iterate fastest.
Deliverables I expect
• Reproducible code or notebook with comments
• Forecast plots and underlying point estimates through the next 6–12 periods
• A short write-up explaining model choice, validation approach, and how to refresh the model with new data
I’ll test the work by running the code on a fresh data pull and comparing your predictions against our held-out set, so please keep dependencies minimal and clearly documented. If you have questions about column definitions or want sample data before we kick off, just let me know.
You will receive the cleaned CSV extracts along with a brief on the variables that matter most. I’m leaning toward classic and modern time-series techniques—ARIMA, SARIMA, Prophet, or any other method you can justify—so long as the model you build is defensible and produces error metrics we can trust. Python (pandas, NumPy, scikit-learn) or R (forecast, tidyverse) are both fine; choose whichever lets you iterate fastest.
Deliverables I expect
• Reproducible code or notebook with comments
• Forecast plots and underlying point estimates through the next 6–12 periods
• A short write-up explaining model choice, validation approach, and how to refresh the model with new data
I’ll test the work by running the code on a fresh data pull and comparing your predictions against our held-out set, so please keep dependencies minimal and clearly documented. If you have questions about column definitions or want sample data before we kick off, just let me know.