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Artificial Intelligence in Forecasting Tools and Techniques 1st Edition by Sachi Mohanty PDF
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. 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.
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