Weather Data Visualization and Prediction with TensorFlow.js

Weather Data Visualization and Prediction with TensorFlow.js


This demo showcases

The data used in this demo is the Jena weather archive dataset.


  1. To visualize and explore the data that goes into the model, select various columns of the dataset in the "Data series 1" and "Data series 2" dropdown menus. Experiment with normalization and no-normalization options. Try showing the data at different time spans. At time spans narrower than "full", you can use the left and right arrow buttons to navigate along the time axis. Finally, try plotting two data series against each other as a scatter plot by checking the "Plot against each other" checkbox.
  2. To train a linear-regression model or a multilayer perceptron (MLP), specify the number of training epochs and click "Train model". Wait patiently for the training to finish. Loss values from the training and validation dataset will be refreshed in the tfjs-vis visor surface on the right-hand side of the page at the end of every training epoch. Experiment with regularization and dropout and observe their effects on overfitting.


Data Visualization

Time span:
Data series 1:
Data series 2:
Normalize data
Plot against each other

Model training

Model Type:
Include date and time features