SignalPop products now support Temporal Fusion Transformer architectures via the new TFT Models!

SignalPop builds probabilistic forecasting and decision systems for organizations that operate under uncertainty – in energy, finance, and operations.  For C# solutions, the open-source MyCaffe platform, with its native Temporal Fusion Transformer, is used as a foundation.  For Python solutions PyTorch and its many time-series and TFT models are used extensively.

Click on each element below for more information on our products.

Easily create and manage datasets via the Dataset Creators.

Visual dataset analysis via Iterative PCA or t-SNE algorithms.

Visual editing of your model via drag-n-drop operations.

Visual blob debugging to view the data and diff contents of each blob flowing between layers.

Real-time debugging which allows you to view the data flowing through your network as you train.

Easy visual debugging to show what each layer of the model sees.

Visual blob debugging to view the data and diff contents of each blob flowing between layers.

To learn more, check out our documents, download our products and try our tutorials.

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