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NeuralProphet is definitely an upgrade for those who are using Prophet for time-series forecasting. NeuralProphet can be applied to β―both - single step - multi-step-ahead time-se...
.@scikit_learn remains one of the most popular ML frameworks. However, building time-series forecasting in scikit-learn requires putting a lot of disjointed components together....
Forecasting high-dimensional time series plays a crucial role in many applications like: - demand forecasting - financial predictions You can use @AmazonScience's DeepGLO for the...
How do transformers work with long text sequences? Actually, they're pretty challenging for transformers. It is due to the self-attention mechanisms transformers use. Here is how...
TAPAS extends BERTβs architecture to work with tabular datasets. Querying tabular data looks trivial but results in a nightmare in the real world.β¬οΈ https://t.co/NmsZjjQeyF
What is Google BERT? Of course, it's one of the most famous models of the transformer generation. But solely classifying BERT as a transformer model would be misleading.β¬οΈ https:...
Add Deep Learning models to your Time-Series Forecasting. You can easily do that using PyTorch Forecasting. It is one of the most interesting new projects in the time-series fore...