A 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 make predictions or decisions based on input data. They are a fundamental component of machine learning and deep learning, enabling tasks such as image recognition, language processing, and pattern recognition.

At its core, an MLP is a type of artificial neural network characterized by its multiple layers. Don’t worry if that sounds a bit technical — we’ll unravel the layers one by one. Picture it like a stack of interconnected nodes, each layer transforming input into meaningful output.

An MLP typically consists of three main layers:

• Input layer: This is where the network receives information. Each node in this layer represents a feature of your input data.
• Hidden layers: The magic happens here. These layers process the input data through a series of weights and activation functions, extracting patterns and relationships.
• Output layer: The final layer produces the network’s…