Название: Artificial Intelligence in Forecasting: Tools and TechniquesАвтор: Sachi Nandan Mohanty, Preethi Nanjundan, Tejaswini Kar
Издательство: CRC Press
Год: 2024
Страниц: 365
Язык: английский
Формат: pdf (true)
Размер: 12.4 MB
Forecasting deals with the uncertainty of the future. To be effective, forecasting models should be timely available, accurate, reliable, and compatible with existing database. Accurate projection of the future is of vital importance in supply chain management, inventory control, economic condition, technology, growth trend, social change, political change, business, weather forecasting, stock price prediction, earthquake prediction, etc. AI powered tools and techniques of forecasting play a major role in improving the projection accuracy. The software running AI forecasting models use Machine Learning to improve accuracy. The software can analyse the past data and can make better prediction about the future trends with higher accuracy and confidence that favours for making proper future planning and decision. In other words, accurate forecasting requires more than just the matching of models to historical data.
Artificial Intelligence (AI) has revolutionized the way forecasting is made in many industries, from finance to retail to healthcare. In today’s fast-paced world, accurate forecasting is essential for businesses to make informed decisions and stay ahead of the competition. The article looks at different tools and techniques used in AI for Forecasting and highlights their advantages and limitations. One of the most widely used AI tools for forecasting is Machine Learning (ML). ML is a subset of AI that allows computer systems to learn from data without being explicitly programmed. Forecasting trains ML algorithms on historical data to identify patterns and relationships that can be used to predict future outcomes. Many ML techniques are available, including regression analysis, decision trees, and neural networks. Each technique has advantages and disadvantages, and the choice of the technique depends on the problem at hand. Regression analysis is a widely used ML technique for forecasting. This involves fitting a line or curve to past data points and using that line or curve to predict future results. Regression analysis can be linear or nonlinear and is especially useful when there is a clear relationship between the independent and dependent variables. Decision trees are another ML technique used for forecasting.
A decision tree creates a tree-like model of decisions and their consequences. Each decision node in the tree represents a choice and each outcome node represents a possible outcome. Decision trees help you identify the most important factors, so they are especially useful when multiple factors can affect your results. However, decision trees can be very complex and difficult to interpret, especially if there are many decision nodes.
Chapter 1: Explores the promises and limitations of AI-powered forecasting. It examines how AI can be used to make predictions in various fields and highlights the need for transparency and ethical considerations in AI development.
Chapter 2: Explores the use of a Multilayered Feed-forward Neural network architecture for accurate rainfall forecasting. The approach utilizes historical rainfall data and meteorological variables as input to predict future rainfall patterns. The study evaluates the effectiveness of the proposed method using various performance metrics.
Chapter 3: Explores the use of Artificial Intelligence techniques for forecasting the stock market index. This includes machine learning algorithms, neural networks, and Deep Learning models to analyze historical data and make predictions about future market trends.
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Chapter 22: Explores the use of AI in improving weather forecasting accuracy and analyzing climate behavior patterns. AI techniques such as ML and Deep Learning can help predict extreme weather events, climate trends, and their impact on the environment, enabling better decision-making and resource management.
The book covers the latest techniques used by managers in business today, discover the importance of forecasting and learn how it's accomplished. Readers will also be familiarised with the necessary skills to meet the increased demand for thoughtful and realistic forecasts.
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