Differencing is a technique used to make a time series stationary by removing trends or seasonality. It involves subtracting the value of an observation from the previous one. For example, if the original series is [100, 120, 130, 150], the first differenced series becomes [20, 10, 20]. This process is key to applying models like ARIMA that require stationarity. Stationarity means that a time series has a constant mean, variance, and autocorrelation over time. Many real-world datasets, such as sales or temperature data, have trends that need differencing to stabilize them. Without stationarity, model predictions may be inaccurate. Differencing can be applied multiple times, but over-differencing should be avoided as it can introduce noise into the data. Checking plots or performing statistical tests like the Augmented Dickey-Fuller (ADF) test can help confirm whether differencing is sufficient. For instance, a time series that shows a downward trend may need first-order differencing, while seasonal patterns might require seasonal differencing.
What is differencing in time series, and why is it used?
- Natural Language Processing (NLP) Basics
- Getting Started with Zilliz Cloud
- Advanced Techniques in Vector Database Management
- Evaluating Your RAG Applications: Methods and Metrics
- 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
What trade-offs exist between model complexity and interpretability?
When developing machine learning models, a key trade-off exists between model complexity and interpretability. Model com
How is SQL used in data analytics?
SQL, or Structured Query Language, is a fundamental tool used in data analytics for managing and manipulating data store
How do recommender systems predict long-tail items?
Recommender systems predict long-tail items by utilizing a combination of collaborative filtering, content-based filteri