# Modeling Multivariate Time Series

Explore reshaping multivariate time series for Conv2D network modeling to capture complex spatial and temporal dependencies.

## We'll cover the following

In the previous lesson, we constructed convolutional neural networks following the conventional approach of placing the temporal axis along a spatial axis and the multivariate features as channels.
Another approach is placing the features along a second spatial axis. This can be done by reshaping the input samples from `(timesteps, features)`

to `(timesteps, features, 1)`

tensors. The reshaping is explained in the illustration below. The reshaped time series appears in the shape of an image with a single channel. Therefore, this approach is termed as modeling a multivariate time series as an image.

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