Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions (English Edition)

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Publisher ‏ : ‎ BPB Publications (October 15, 2021)
Language ‏ : ‎ English
Paperback ‏ : ‎ 314 pages
ISBN-10 ‏ : ‎ 9391392571
ISBN-13 ‏ : ‎ 978-9391392574
Item Weight ‏ : ‎ 1.3 pounds
Dimensions ‏ : ‎ 7.5 x 0.71 x 9.25 inches

Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions (English Edition)

In this post, we will explore the powerful combination of PyTorch, Recurrent Neural Networks (RNN), Temporal Convolutional Networks (TCN), and Deep Neural Network models for time series forecasting. We will discuss how these different deep learning architectures can be used together to create accurate and production-ready prediction solutions.

Time series forecasting is a critical task in many industries, including finance, retail, healthcare, and more. Traditional forecasting methods often struggle to capture the complex patterns and relationships present in time series data. Deep learning models, with their ability to learn intricate patterns and dependencies, have emerged as a promising approach for time series forecasting.

PyTorch is a popular deep learning framework known for its flexibility and ease of use. By leveraging PyTorch’s capabilities, we can easily implement and train deep learning models for time series forecasting. RNNs, TCNs, and DNNs are all powerful architectures that excel at capturing temporal dependencies in sequential data. By combining these models, we can create a robust forecasting system that can handle a wide range of time series datasets.

In this post, we will cover the fundamentals of time series forecasting and deep learning, introduce the PyTorch framework, and walk through the implementation of RNN, TCN, and DNN models for time series forecasting. We will discuss best practices for training and evaluating these models, and demonstrate how they can be integrated into a production-ready prediction system.

Whether you are a data scientist looking to improve your forecasting models or a business looking to leverage the power of deep learning for time series prediction, this post will provide you with the knowledge and tools you need to succeed. Stay tuned for more updates on how to make accurate and reliable time series forecasts using deep learning techniques.
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