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<p>Created page with &quot;==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...&quot;</p> <p><b>New page</b></p><div>==Introduction==<br /> 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.<br /> <br /> ==Types of Nonstationarity==<br /> Machine learning can encounter many types of nonstationarity, including:<br /> <br /> Trend nonstationarity is a phenomenon in which the data&#039;s mean changes over time. This can be caused either by changes in economic conditions, or shifts in consumer behaviour.<br /> *Cyclical nonstationarity is when the data shows patterns of repetition such as seasonal fluctuations and business cycles.<br /> Volatility nonstationarity is a condition in which the data&#039;s variance changes over time. This can be caused either by changes in market conditions, or shifts in investor mood.<br /> *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.<br /> <br /> ==Methods to handle Nonstationarity==<br /> There are many ways to deal with nonstationarity in machine-learning, including:<br /> <br /> *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.<br /> *Detrending: This is the process of removing the trend from data using techniques like polynomial regression and moving averages.<br /> *Seasonal decomposition - This involves separating the data into its seasonal, trend, and residual components.<br /> *State space models: This model models time series as a combination underlying latent variables, and a set observed variables.<br /> *Adaptive filtering is a method that automatically adjusts its parameters to account changes in the statistical properties of the data.<br /> <br /> ==Explain Like I&#039;m 5 (ELI5)==<br /> 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.</div>

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