The ARIMA model (AutoRegressive Integrated Moving Average) is a popular statistical method used for time series forecasting. It combines three key components: (1) AutoRegression (AR), which uses the relationship between an observation and its past values; (2) Differencing (I), which makes the time series stationary by removing trends or seasonality; and (3) Moving Average (MA), which models the relationship between an observation and a residual error from a moving average model. Together, these components allow ARIMA to capture both the patterns and randomness in a time series. For example, ARIMA is often used to forecast sales, stock prices, or energy usage based on historical data. The ARIMA model requires the time series to be stationary. A stationary series has constant mean, variance, and autocorrelation over time. If the series isn’t stationary, differencing is applied to transform it. ARIMA is defined by three parameters: (p, d, q), where p is the order of the AR part, d is the degree of differencing, and q is the order of the MA part. Selecting these parameters correctly is critical to creating an accurate model. ARIMA is versatile but assumes linear relationships in data. For more complex datasets, extensions like SARIMA (Seasonal ARIMA) handle seasonality, while ARIMA combined with machine learning can address nonlinear patterns. This adaptability makes ARIMA widely used in many industries.
What is the ARIMA model in time series analysis?

- Natural Language Processing (NLP) Advanced Guide
- Getting Started with Zilliz Cloud
- Information Retrieval 101
- Retrieval Augmented Generation (RAG) 101
- Getting Started with Milvus
- 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 computer vision enable industrial monitoring?
Computer vision enables industrial monitoring by automating the inspection and analysis of operations in real-time. Came
Is computer vision still in early stage as a science?
While computer vision has a long history dating back to the 1960s, it has only recently reached a level of maturity wher
How do SSL models differ from traditional deep learning models?
SSL models, or Semi-Supervised Learning models, differ from traditional deep learning models primarily in how they utili