Nonstationary

Introduction

Nonstationarity in machine-learning refers to the notion that the statistical properties of a system, or dataset, change over time. This can make it difficult for traditional machine learning techniques to predict or model the behavior of the system. These techniques assume that the data is static.

Types of Nonstationarity

Machine learning can encounter many types of nonstationarity, including:

Trend nonstationarity is a phenomenon in which the data's mean changes over time. This can be caused either by changes in economic conditions, or shifts in consumer behaviour.

• Cyclical nonstationarity is when the data shows patterns of repetition such as seasonal fluctuations and business cycles.

Volatility nonstationarity is a condition in which the data's variance changes over time. This can be caused either by changes in market conditions, or shifts in investor mood.

• Structural Nonstationarity: This is when the relationships between variables in data change over time. This can be caused either by technological changes or shifts within the industry structure.

Methods to handle Nonstationarity

There are many ways to deal with nonstationarity in machine-learning, including:

• Differencing is the process of subtracting the time series value at a previous point from the current value. This can be used for removing trends from the data.
• Detrending: This is the process of removing the trend from data using techniques like polynomial regression and moving averages.
• Seasonal decomposition - This involves separating the data into its seasonal, trend, and residual components.
• State space models: This model models time series as a combination underlying latent variables, and a set observed variables.
• Adaptive filtering is a method that automatically adjusts its parameters to account changes in the statistical properties of the data.

Explain Like I'm 5 (ELI5)

Machine learning is nonstationarity. This means that data can change over time. The amount of ice cream sold in a store might be different on a hot summer day than it is on a cold winter morning. Machine learning models must be able to adapt to these changes and make predictions accordingly. This can be done in a number of ways, including removing the trend or breaking down the data into smaller pieces, such as summer and winter data.