Time Series Analysis

In the previous chapter, we introduced AI, machine learning, and deep learning. We also discovered how the banking sector functions and how the use of AI can enhance banking processes. We learned the importance of banking processes being easily accessible. We also learned about a machine learning modeling approach called CRISP-DM. Overall, the chapter provided the necessary background for the application of machine learning in the banking industry to solve various business problems.

In this chapter, we will learn about an algorithm that analyzes historical data to forecast future behavior, known as time series analysis. Time series analysis works on the basis of one variable—time. It is the process of capturing data points, also known as observations, at specific time intervals. The goal of this chapter is to understand time series analysis in detail through examples and explain how Machine-to-Machine (M2M) communication can be helpful in the implementation of time series analysis. We will also understand the concepts of financial banking as well.

In this chapter, we will cover the following topics :

• Understanding time series analysis
• M2M communication
• The basic concepts of financial banking
• AI modeling techniques
• Demand forecasting using time series analysis
• Procuring commodities using neural networks on Keras