MACHINE LEARNING. SUPERVISED LEARNING TECHNIQUES; TIME SERIES FORECASTING. EXAMPLES WITH NEURAL NETWORKS AND MATLAB

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Publisher ‏ : ‎ Scientific Books (November 25, 2020)
Language ‏ : ‎ English
Paperback ‏ : ‎ 277 pages
ISBN-10 ‏ : ‎ 1716411777
ISBN-13 ‏ : ‎ 978-1716411779
Item Weight ‏ : ‎ 1.07 pounds
Dimensions ‏ : ‎ 6 x 0.63 x 9 inches

Machine Learning: Supervised Learning Techniques; Time Series Forecasting

In the world of machine learning, supervised learning techniques are widely used for predicting future outcomes based on historical data. One specific application of supervised learning is time series forecasting, where the goal is to predict future values of a time-dependent variable based on past observations.

One popular approach for time series forecasting is using neural networks, which are computational models inspired by the way the human brain processes information. Neural networks have the ability to learn complex patterns in data and make predictions based on these patterns.

In this post, we will explore examples of time series forecasting using neural networks in MATLAB, a powerful software tool commonly used for numerical computing and data analysis.

Example 1: Stock Price Prediction

One common application of time series forecasting is predicting stock prices. By analyzing historical stock price data, a neural network can learn patterns and trends in the data to make predictions about future stock prices.

To build a neural network model for stock price prediction in MATLAB, you can use the Neural Network Toolbox, which provides tools for creating, training, and evaluating neural networks. You can train the neural network using historical stock price data and then use the model to make predictions about future stock prices.

Example 2: Demand Forecasting

Another example of time series forecasting is demand forecasting, where the goal is to predict future demand for a product or service based on past sales data. By using a neural network model in MATLAB, you can analyze historical sales data and make predictions about future demand for the product.

To create a neural network model for demand forecasting in MATLAB, you can use the time series forecasting tools available in the Statistics and Machine Learning Toolbox. These tools allow you to preprocess the data, train the neural network model, and evaluate its performance in making predictions about future demand.

In conclusion, supervised learning techniques, such as neural networks, are powerful tools for time series forecasting. By using MATLAB, you can build and train neural network models to make accurate predictions about future outcomes based on historical data. Whether you are predicting stock prices or forecasting demand, neural networks in MATLAB can help you make informed decisions and drive better business outcomes.
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