deep learning

Multiple Time Series Forecasting with DeepAR in Python

In this post, we will learn how to use DeepAR to forecast multiple time series using GluonTS in Python. DeepAR is a deep learning algorithm based on recurrent neural networks designed specifically for time series forecasting. It works by learning a model based on all the time series data, instead of creating a separate model for each one. In my experience, this often works better than creating a separate model for each time series....

Multiple Time Series Forecasting with Convolutional Neural Networks in Python

In this article you will learn an easy, fast, step-by-step way to use Convolutional Neural Networks for multiple time series forecasting in Python. We will use the NeuralForecast library which implements the Temporal Convolutional Network (TCN) architecture. Table of Contents Temporal Convolutional Network (TCN) How to Install NeuralForecast With and Without GPU Support How To Prepare Time Series Data For The Temporal Convolutional Network Time Series Validation Split Temporal Convolutional Network Hyperparameters kernel_size dilations input_size_multiplier encoder_hidden_size context_size decoder_hidden_size learning_rate max_steps Training a Temporal Convolutional Network In Python Training a Temporal Convolutional Network with External Variables in Python Temporal Convolutional Network (TCN) This architecture is a variant of the Convolutional Neural Network (CNN) architecture that is specially designed for time series forecasting....

Multiple Time Series Forecasting With LSTM In Python

Building an LSTM from scratch is very laborious and prone to mistakes, but we have libraries that will help us harness its power without having to spend hours debugging. We will use the NeuralForecast library which, in addition to implementing the most common structures of recurrent neural networks, also implements automatic hyperparameter tuning. Table of Contents How Does an LSTM Work? When to Use an LSTM How to Install NeuralForecast With and Without GPU Support How To Prepare Time Series Data For The LSTM Time Series Validation Split Automatic Time Series Modeling With LSTM NeuralForecast’s LSTM Architecture Automatic Hyperparameter Tuning input_size_multiplier encoder_hidden_size encoder_n_layers decoder_hidden_size learning_rate max_steps Training Code For LSTM In Python Hyperparameter Tuning Report Adding External Variables To The LSTM Custom PyTorch Loss Function (WMAPE) {#wmape-loss} Frequently Asked Questions How To Train The LSTM With Multiple SKUs?...