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Time series problems are very important in the industry and academia. Many commercial applications use time series data, such as stock market analysis and predicting consumer behavior in retail. Time series data is used extensively in weather forecasting, stock market prediction, demand forecasting, and many more applications.The landscape in machine learning for time series has been changing and many different libraries and algorithms are out there to deal with time series. Some popular methods for time series include ARIMA, LSTM, Prophet, and SARIMA. This book is trying to help choose the right tools and approaches for specific situations.In this chapter, we'll discuss time series and the typical set of problems associated with them in more detail. After reading this chapter, you should become familiar with the following:
- What is a time series?
- Which sets of techniques are there for time series?
- What are the different technical problems associated with time series?
- What are the sets of techniques and approaches for time series?
- How can we approach time series problems?
We'll conclude with a discussion of some historical development of time series and of the challenges and future directions in time series data analysis.
