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A time-series forecasting and anomaly-detection tool
I am an Industrial Engineer utilizing the power of python to gain deeper insights in data.
I am currently learning Deep learning with TensorFlow
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A time-series forecasting and anomaly-detection tool that lets users upload any dataset, automatically identifies the date and value columns, and produces dual forecasts with Prophet and auto-tuned SARIMAX—complete with Isolation Forest anomaly overlays.
In many datasets, timelines and trends are everything—yet I often bounce between separate scripts for forecasting, outlier hunting, and visualization. I wanted a single, browser-based workspace where anyone could upload a CSV or Excel file and instantly see forward-looking forecasts and anomaly flags in one place. I built this tool utilizing Streamlit.
➡️ Check out the Streamlit dashboard.
One-Click Data Ingestion
Drop in any time-series file (or play with the built-in sample). The app automatically sniffs out the date/time and metric columns, even if you rename or reorder them.
Dual Forecast Engines
Prophet—great for strong seasonal patterns and holiday effects.
Auto-tuned SARIMAX—handles subtle autocorrelation structures.
Both models train side-by-side, and their prediction intervals are plotted together for comparison.
Isolation Forest Anomaly Layer
After training, an Isolation Forest scans historical residuals plus new forecasts, shading points that deviate beyond an adaptive threshold.
Interactive Plotly Visuals
Hover to inspect values, toggle series on/off, zoom, or download a PNG snapshot.
Instant Exports
Click once to grab a tidy CSV of both forecasts or save the current chart to PNG for slide decks.
The repo is open-sourced on GitHub and deploy-ready to Streamlit Cloud in under five minutes. Clone, push, and share a public link with stakeholders—no server wrangling required.
Check out the Streamlit dashboard. Also check out my other times-series projects.
Let me know what you think!