Abstract
Artificial intelligence (AI) has gained considerable achievements over the last decades, and AI methods have been proposed as alternatives to statistical ones for time series forecasting and classification. This chapter introduces some latest advancements in this respect, including artificial neural networks and deep learning, Google’s TensorFlow, and more. Now the Python package Keras has become a module of TensorFlow as a frontend. We also discuss how to use TensorFlow and write Python code to implement time series forecasting. Note that there are a lot of terms in this chapter from the fields of artificial intelligence and computer science.
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Notes
- 1.
The following Python code is also validated with Python of V. 3.9.7, TensorFlow of V. 2.7.0, and statsmodels of V. 0.13.1.
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Huang, C., Petukhina, A. (2022). Modern Machine Learning Methods for Time Series Analysis. In: Applied Time Series Analysis and Forecasting with Python. Statistics and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-13584-2_10
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