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?

- The Definitive Guide to Building RAG Apps with LlamaIndex
- Large Language Models (LLMs) 101
- Mastering Audio AI
- Exploring Vector Database Use Cases
- GenAI Ecosystem
- 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 SSL impact the development of AI in healthcare?
SSL, or Secure Sockets Layer, significantly impacts the development of AI in healthcare by providing a secure framework
What is Augmented Reality (AR) and how does it work?
Augmented Reality (AR) is a technology that superimposes digital information—such as images, sounds, and text—onto the r
What is PyTorch, and how is it used in deep learning?
PyTorch is an open-source machine learning library widely used for deep learning applications. Developed by Facebook's A