Yes, there are several standard RNN architectures that have been widely applied to time series analysis, forecasting, and anomaly detection problems. These architectures include basic RNNs, Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs).

Azam Amir : Do you know names of some standard architectures for the same ?

Yes, Recurrent Neural Networks (RNNs) have been extensively used in time series analysis. RNNs are particularly well-suited for this type of data due to their ability to handle sequential information and maintain memory of previous inputs, which is crucial for time-dependent data.

Applications of RNNs in Time Series Analysis

Types of RNNs Used

Benefits of Using RNNs for Time Series Analysis

• Memory of Previous Inputs: RNNs inherently consider the sequential nature of time series data, retaining information from previous time steps.
• Handling Variable-Length Sequences: RNNs can process sequences of varying lengths, making them versatile for different types of time series data.
• Capturing Temporal Dependencies: They can capture complex temporal dependencies, which are essential for accurate time series prediction and analysis.

Challenges and Considerations

• Computational Intensity: Training RNNs, especially LSTMs and GRUs, can be computationally intensive.
• Need for Large Datasets: RNNs typically require large datasets to learn effectively, which may not always be available.
• Overfitting: They can overfit to the training data, especially if the time series data is noisy or the model is overly complex.

Great Chuck A Arize

Try this article - it contains examples that may be a clue to reference architectures:

Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L. & Muller, P. A.. 2019. Deep learning for time series classification: a review. Data Mining and Knowledge Discovery 33: 917-963. doi: 10.1007/s10618-019-00619-1. ://WOS:000471246600004

https://link.springer.com/article/10.1007/s10618-019-00619-1

Let me look into it Michael Hopmere

Yes, Recurrent Neural Networks (RNNs) have been widely used in time series analysis. They are effective for modeling sequential data and capturing temporal dependencies, making them suitable for tasks like forecasting, anomaly detection, and sequence prediction.

Thanks Chuck A Arize