“Enhancing Time Series Classification with Efficient Feature Extraction Methods”
Introduction:
In the world of time series classification, there are two main approaches: using time series specific methods or extracting features and utilizing regular supervised learning. This article focuses on the latter, exploring how to automatically extract relevant features using a Python package called tsfresh.
The datasets used in this study are sourced from the Time Series Classification Repository, which provides information on the best accuracy achieved for each dataset. The results obtained using tsfresh show promising performance comparable to or even better than the state of the art accuracy.
When dealing with time series data, the examples are not independent due to their codependency. For instance, temperature today will likely not be drastically different from tomorrow. This dependence violates the assumption of traditional classifiers. Additionally, time series data is structured in a hierarchical manner, with multiple attributes within each time step.
However, by extracting features, a time series can be reduced to a single point. For example, instead of representing daily weather over a month as a series of temperature measurements, we can use minimum temperature, maximum temperature, average temperature, and so forth as features. Developing and implementing such features can be tedious, but tsfresh simplifies this process by automatically extracting a vast range of features.
Furthermore, tsfresh offers feature selection capabilities to choose the most predictive features. It is crucial to perform feature selection on the training dataset to prevent label leakage and obtain unbiased evaluation results on the validation set.
In practical implementation, three major datasets were used: FordA, FordB, and Wafer. The time series from these datasets were one-dimensional, with each series represented as a row in a CSV file, and columns indicating time steps. Consequently, data reshaping was necessary to match the tsfresh format.
Feature extraction and selection are computationally intensive tasks, so tsfresh performs them in parallel. It is important to structure the code accordingly to avoid issues with multiprocessing. By default, tsfresh also accounts for null values in feature construction by providing the impute() function.
During feature selection, a hyperparameter called fdr_level can be tuned. This parameter represents the expected percentage of irrelevant features among all created features. Depending on the downstream classifier’s ability to handle non-informative features, it may be necessary to adjust the fdr_level to balance the number of selected features against the examples to features ratio.
Once the features are selected, various classifiers can be trained and evaluated. Logistic regression on scaled features is a commonly employed technique. The complete code for this study, compatible with binary classification datasets from the Time Series Repository, is available on GitHub.
In summary, tsfresh simplifies the feature extraction and selection process for time series classification, yielding competitive results when compared to the state of the art. By automatically extracting relevant features and incorporating feature selection techniques, tsfresh assists in building accurate and efficient time series classifiers.
Full Article: “Enhancing Time Series Classification with Efficient Feature Extraction Methods”
Automatically Extracting Relevant Features with tsfresh for Time Series Classification
When it comes to classifying time series data, there are two main options available. One approach is to use a time series specific method, such as Long Short-Term Memory (LSTM) or a recurrent neural network. The other option is to extract features from the series and use them for normal supervised learning. In this article, we will explore how to automatically extract relevant features using a Python package called tsfresh.
The datasets used in this study were obtained from the Time Series Classification Repository, which provides information on the best accuracy achieved for each dataset. The results obtained from using tsfresh with these datasets were close to or even better than state-of-the-art results.
Time series data presents a unique challenge in classification tasks because the examples are not independent. The closer in time the examples are to each other, the more correlated they become. For example, if today’s temperature is 20 degrees Celsius, it is more likely to be around 15 or 25 degrees tomorrow rather than 5 or 35 degrees.
This dependency among examples makes it impossible to use a normal classifier, as they assume independence among examples. Additionally, the structure of time series data is one level deeper than traditional classification tasks. Each example is not just a single point, but a time series consisting of multiple points or steps. These steps may contain various attributes, such as temperature, humidity, and wind speed.
However, it is possible to reduce a time series to a single point by extracting relevant features. For instance, if we have a time series of daily weather over a month, we can extract features such as minimum temperature, maximum temperature, average temperature, median temperature, and variance of temperatures. The list of possible features is extensive. Implementing and inventing these features manually can be time-consuming and tedious. Luckily, tsfresh is a Python package that can automatically extract a large number of features.
It’s important to note that while tsfresh extracts a multitude of features, not all of them may be relevant. To ensure we only select features with predictive power, tsfresh has built-in feature selection capabilities. However, it’s crucial to avoid label leakage during this step. To do so, the dataset should be split into training and validation sets, and only the training set should be used for feature selection. Otherwise, the validation results may be overly optimistic.
Tsfresh uses pairwise significance tests to select features. However, if the target variable is solely determined by the interaction of features and not any individual feature, this approach may pose a problem.
In the practical implementation of tsfresh, we applied it to three of the largest datasets available: FordA, FordB, and Wafer. The time series data from the repository are all one-dimensional, such as temperature or humidity, but not both. In this setup, each series is represented as a row in a CSV file, with columns representing time steps.
To preprocess the data and format it in a way that is compatible with tsfresh, we need to reshape the data by stacking the values, renaming the index, and resetting it to a new format.
The feature extraction and selection steps can be computationally intensive, so tsfresh provides options for parallel processing. However, it’s important to structure the code using the “if __name__ == ‘__main__’:” style to avoid issues with multiprocessing. Alternatively, you can set the “n_jobs” parameter to 1 for single-threaded processing.
During feature extraction, some feature constructors may output null values. To address this, tsfresh provides the “impute()” function to handle null values. It’s important to ensure that all null values are imputed before proceeding.
When it comes to feature selection, there is a hyperparameter called “fdr_level” that determines the expected percentage of irrelevant features among all created features. By default, it is set to a low value of 5%. However, depending on the number of features obtained from selection and the downstream classifier’s ability to handle non-informative features, this value can be increased to 0.5 or even 0.9. It’s important to find the right balance between the number of examples and features to avoid the curse of dimensionality and ensure the best generalization.
Once the feature extraction and selection are complete, it’s time to train and evaluate classifiers. Logistic regression on scaled features is typically a good choice for time series classification tasks. The complete code for this project is available on GitHub, and it should work with all binary classification datasets from the Time Series Repository once the ARFF headers are removed from the CSV files.
In conclusion, the tsfresh package provides an automated and efficient way to extract relevant features from time series data for classification tasks. With its built-in feature selection capabilities, it is possible to identify features with predictive power and improve the accuracy of classification models. The ability to parallelize the computations further enhances the efficiency of the feature extraction process. By leveraging tsfresh, researchers and data scientists can focus on the analysis and interpretation of the extracted features, rather than spending valuable time on manual feature engineering.
Summary: “Enhancing Time Series Classification with Efficient Feature Extraction Methods”
When classifying time series data, there are two options: using a time series specific method like LSTM or extracting features from the series and using them with supervised learning. This article explores how to automatically extract relevant features using a Python package called tsfresh. The package extracts a variety of features automatically, but it is important to select those with predictive power. The article also provides a step-by-step guide on reshaping the data and performing feature extraction and selection using tsfresh. The code provided is applicable to binary classification datasets from the Time Series Classification Repository.
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