A Simplified Guide to Time Series Analysis Using Neural Networks

Creating an MLP Algorithm to Analyze Time Series

In this beginner’s guide, we’ll explore the advantages and limitations of mutliple-layer perceptrons (MLPs), learn how to prepare financial data, and discover the steps to build, train, and evaluate your own forecasting model.

Deep Learning and MLPs in Layman’s Terms

Deep learning is a subset of machine learning, a type of artificial intelligence, that focuses on training neural networks with multiple layers to automatically learn and extract patterns and representations from data. These deep neural networks can understand and make decisions from complex and large datasets, and they are particularly effective for tasks like image recognition, natural language processing, and time series forecasting. Deep learning has revolutionized AI by enabling the development of highly accurate models for a wide range of applications.

Neural networks are computational models inspired by the human brain’s structure and function. They consist of interconnected nodes, called neurons, organized into layers. These networks are designed to process and learn from data. Each connection between neurons has a weight, and neural networks adjust these weights during training to…