# Building a Time-Series Forecast & Anomaly Dashboard

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.

### **Why I Built It**

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](https://unizomby-timeseries-dash-app-xkbqpe.streamlit.app/).

### **What the App Does**

1. **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.
    
2. **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.
        
3. **Isolation Forest Anomaly Layer**
    
    After training, an Isolation Forest scans historical residuals plus new forecasts, shading points that deviate beyond an adaptive threshold.
    
4. **Interactive Plotly Visuals**
    
    Hover to inspect values, toggle series on/off, zoom, or download a PNG snapshot.
    
5. **Instant Exports**
    
    Click once to grab a tidy CSV of both forecasts or save the current chart to PNG for slide decks.
    

[![](https://cdn.hashnode.com/res/hashnode/image/upload/v1750521387701/3539f530-2126-49c7-ab72-d6816d4bc9ff.png align="center")](https://unizomby-timeseries-dash-app-xkbqpe.streamlit.app/)

### **Try It Yourself**

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](https://unizomby-timeseries-dash-app-xkbqpe.streamlit.app/). Also check out my other times-series projects.

Let me know what you think!

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