How can you handle non-stationary data in machine learning for engineering design?

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Machine learning (ML) is a powerful tool for engineering design, as it can learn from data and generate solutions that meet complex requirements and constraints. However, not all data are static and stable. Sometimes, the data may change over time due to environmental factors, user feedback, or system updates. This is called non-stationary data, and it poses a challenge for ML models that assume the data distribution is fixed and independent. How can you handle non-stationary data in ML for engineering design? Here are some tips and techniques to consider.

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1 Identify the type of non-stationarity

Non-stationary data can be classified into different types, depending on the nature and frequency of the changes. For example, some data may exhibit gradual drift, where the data distribution changes slowly and smoothly over time. Other data may show abrupt shift, where the data distribution changes suddenly and significantly at some point. Some data may have recurring patterns, where the data distribution repeats itself periodically or cyclically. And some data may have noise or outliers, where the data distribution is distorted by random or erroneous observations. Identifying the type of non-stationarity can help you choose the appropriate ML method and evaluation metric.

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Ramya Gundavajhula

Data Science Engineer
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In my experience non-stationary data is very common in stochastic time series data models.Non-stationarity in any real time time series is generally in all 3 statistical parameters-mean, variance and covariance. Plotting the time series might prove to be useful to identify in cases when we are aware of trends or seasonality ahead.For exploratory data analysis it gets imperative to study the time series using statistical hypothesis testing that involve checking for unit roots such as the Augmented-Dickey-Fuller test and KPSS test to check for stationarity along with data visualization.The two tests in combination can give us reasonable confidence to determine whether a series can be transformed into a strict, trend or difference stationary.

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2 Adapt the ML model to the data changes

One way to handle non-stationary data is to adapt the ML model to the data changes, so that the model can learn from the most relevant and recent data. This can be done by using online learning, where the model is updated continuously or incrementally as new data arrives. Online learning can be implemented by using different algorithms, such as stochastic gradient descent, online support vector machines, or online neural networks. Online learning can also be combined with forgetting mechanisms, where the model discards or discounts old or irrelevant data, or with ensemble methods, where the model combines multiple sub-models that are trained on different subsets of data.

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3 Detect and react to the data changes

Another way to handle non-stationary data is to detect and react to the data changes, so that the model can adjust its parameters or structure when a significant change occurs. This can be done by using change detection, where the model monitors the data stream and identifies the points or intervals where the data distribution changes. Change detection can be implemented by using different techniques, such as statistical tests, control charts, or distance measures. Change detection can also be combined with adaptation strategies, where the model re-trains, re-samples, or re-weights the data after a change is detected.

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4 Evaluate the ML model under non-stationarity

A final step to handle non-stationary data is to evaluate the ML model under non-stationarity, so that you can measure the performance and robustness of the model over time. This can be done by using evaluation metrics that account for the temporal dynamics of the data, such as accuracy over time, concept drift rate, or forgetting factor. Evaluation metrics can also be combined with visualization tools, such as learning curves, confusion matrices, or performance plots, to provide a comprehensive and intuitive overview of the model behavior and performance under non-stationarity.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you handle non-stationary data in machine learning for engineering design?

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1 Identify the type of non-stationarity

2 Adapt the ML model to the data changes

3 Detect and react to the data changes

4 Evaluate the ML model under non-stationarity

5 Here’s what else to consider

Engineering Design

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How can you handle non-stationary data in machine learning for engineering design?

1

2

3

4

5

1 Identify the type of non-stationarity

2 Adapt the ML model to the data changes

3 Detect and react to the data changes

4 Evaluate the ML model under non-stationarity

5 Here’s what else to consider

Engineering Design

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