Mean Absolute Error (MAE) is a commonly used metric to evaluate the accuracy of a time series model. It measures the average magnitude of errors between predicted and actual values, providing a straightforward way to understand the model's performance. The formula for MAE is: ( \text{MAE} = \frac{1}{n} \sum_{i=1}^{n}
What is mean absolute error (MAE) in time series forecasting?

- Advanced Techniques in Vector Database Management
- Information Retrieval 101
- AI & Machine Learning
- Natural Language Processing (NLP) Basics
- Retrieval Augmented Generation (RAG) 101
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How does the concept of the “curse of dimensionality” influence the design of indexing techniques for vector search?
The curse of dimensionality—the challenge of analyzing data in high-dimensional spaces—forces indexing techniques for ve
Can anomaly detection improve human decision-making?
Yes, anomaly detection can significantly improve human decision-making. Anomaly detection refers to the process of ident
What are the advantages of deep reinforcement learning over traditional methods?
The main advantage of deep reinforcement learning (DRL) over traditional methods lies in its ability to handle complex a