Selecting parameters for an ARIMA model involves determining p, d, and q through a combination of analysis and testing. Start by identifying if differencing (d) is necessary to make the time series stationary. Perform a unit root test like the Augmented Dickey-Fuller (ADF) test, and if the p-value is high, apply differencing until the series achieves stationarity. A non-stationary series can lead to inaccurate forecasts. Next, identify p (AR order) and q (MA order) by examining the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots. For example, a PACF plot that cuts off after lag k suggests an AR(k) process, while an ACF plot that cuts off indicates an MA process. Trial-and-error can also help fine-tune these parameters. Tools like grid search and information criteria, such as AIC (Akaike Information Criterion) or BIC (Bayesian Information Criterion), assist in evaluating models with different parameters. Use these to balance model complexity and accuracy. Modern libraries like Python's statsmodels simplify parameter selection through built-in functions like auto_arima, which automatically tests combinations of p, d, and q.
How do you choose parameters for an ARIMA model?
- Evaluating Your RAG Applications: Methods and Metrics
- Getting Started with Milvus
- Accelerated Vector Search
- Advanced Techniques in Vector Database Management
- Embedding 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
What is the typical length or detail of a report generated by DeepResearch, and can this be adjusted or controlled?
DeepResearch-generated reports typically range between 10-15 pages (3,000-5,000 words) when covering a standard technica
What are common techniques used in NLP?
Common techniques in NLP can be grouped into three categories: preprocessing, feature extraction, and modeling. Preproce
How do benchmarks evaluate query distribution strategies?
Benchmarks evaluate query distribution strategies by measuring how well a database system can handle different types of