Time Series Forecasting Example Project

1 min
Patrycja Jenkner
21st April, 2022

Here’s a public example project to give you a taste of’s API and the app.

It’s a time series forecasting project that relies on structured tabular data. It shows how to use Neptune with popular time series forecasting algorithms like ARIMA, LSTM, and Prophet.

You can just open the project and play with the app, no registration is needed. And if you want to see the code behind it, go to the example’s GitHub repo

What’s in this time series example project? 

Here are a few most important things you can see in the project: 

If you want to see only the list of runs, you can change the view by clicking the Runs table tab at the top.  The Runs table shows all the experiments (with associated metadata) that were performed when training the model.

You can easily change the columns that are visible in the runs table or filter the runs

  • In the Compare runs tab, you can switch from Charts comparison to Images comparison or Side-by-side comparison. If you’ve logged artifacts, you can also compare datasets between runs (not visible in this example). 
See in the app
Images comparison in the Neptune app

By the way, you can choose which runs you want to compare by clicking the eye icon in the runs table. 

  • You can also inspect a single run.  Click the Runs details tab at the top of your screen, and you’ll land in the single run’s view. Just below the name of the run, you can see different dashboards.

    The first one is the All metadata dashboard with everything that was logged to Neptune (organized in a folder-like structure). 
See in the app
All metadata dashboard in the Neptune app

And then, there are Charts, Images, Monitoring, Source code, and more. 

See in the app
Custom dashboard in the Neptune app

Other useful resources

Explore other example projects, e.g. tabular data example project or text classification example project

Watch the webinar Time-series Forecasting With Model Types: ARIMAX, FBProphet, LSTM, with a live demo. 

Contact us if you’d like to get a live customized demo specific to your use case. 

Couldn’t find the use case you were looking for?

Just get in touch, and our ML team will create a custom demo for you.