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?
- Large Language Models (LLMs) 101
- Exploring Vector Database Use Cases
- Accelerated Vector Search
- AI & Machine Learning
- 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 are best practices for scaling inference in Vertex AI?
To scale inference in Vertex AI, start with right-sizing and isolation. Use separate endpoints for distinct latency/thro
What is the role of regularization in anomaly detection models?
Regularization plays a crucial role in anomaly detection models by preventing overfitting, maintaining model simplicity,
How does full-text search handle stemming exceptions?
Full-text search systems often implement stemming to improve the search experience by reducing words to their base or ro