Technical requirements

The models developed in this chapter are based on different frameworks. First, we show how to develop prediction-based methods using the statsforecast and neuralforecast libraries. Other methods, such as an LSTM AE, will be explored using the PyTorch Lightning ecosystem. Finally, we’ll also use the PyOD library to create anomaly detection models based on approaches such as GANs or VAEs. Of course, we also rely on typical data manipulation libraries such as pandas or NumPy. The following list contains all the required libraries for this chapter:

• scikit-learn (1.3.2)
• pandas (2.1.3)
• NumPy (1.26.2)
• statsforecast (1.6.0)
• datasetsforecast (0.08)
• 0neuralforecast (1.6.4)
• torch (2.1.1)
• PyTorch Lightning (2.1.2)
• PyTorch Forecasting (1.0.0)
• PyOD (1.1.2)

The code and datasets used in this chapter can be found at the following GitHub URL: https://github.com/PacktPublishing/Deep-Learning-for-Time-Series-Data-Cookboo...