A method for predicting a failure of oilfield equipment based on univariate time series includes providing training data by a sensor. A training data stream comprising the training data is received by a preprocessor. The method further includes extracting training data segments and identifying each training data segment of the training data segments as corresponding to a normal operational state of the first oilfield equipment or a failed state of the first oilfield equipment. The method also includes generating a shapelet-based decision tree and receiving a test data stream from a sensor of second oilfield equipment. The method further includes determining, based on the shapelet-based decision tree, whether one or more test data segments extracted from the test data stream predict a failure of the second oilfield equipment.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for predicting a failure of oilfield equipment based on univariate time series, the method comprising: providing, by a sensor of first oilfield equipment, training data, wherein a training data stream comprising the training data is received by a preprocessor; extracting, by the preprocessor, training data segments from one or more portions of the training data stream generated prior to the first oilfield equipment failing to operate normally, wherein portions of the training data in the one or more portions of the training data stream were sampled at a regular sampling rate; identifying each training data segment of the training data segments as corresponding to a normal operational state of the first oilfield equipment or a failed state of the first oilfield equipment, wherein the failed state of the first oilfield equipment corresponds to the oilfield equipment failing to operate normally; generating, by a processor, a shapelet-based decision tree, wherein generating the shapelet-based decision tree includes extracting one or more time series shapelets from one or more training data segments of the training data segments; receiving a test data stream from a sensor of second oilfield equipment; determining by the processor, based on the shapelet-based decision tree, whether one or more test data segments extracted from the test data stream predict a failure of the second oilfield equipment. 2. The method of claim 1, wherein the one or more test data segments are extracted from one or more portions of the test data stream and wherein test data in the one or more portions of the test data stream were sampled at the regular sampling rate. 3. The method of claim 1, wherein determining whether the one or more test data segments predict the failure of the second oilfield equipment based on the shapelet-based decision tree includes determining Euclidean distances between each test data segment of the one or more test data segments and the one or more time series shapelets. 4. The method of claim 1, further comprising predicting the failure of the second oilfield equipment if a ratio of a number of test data segments that predict the failure of the second oilfield equipment processed based on the shapelet-based decision tree to a total number of the one or more test data segments exceeds a threshold. 5. The method of claim 1, wherein extracting the training data segments from the one or more portions of the training data stream includes excluding a portion of the training data stream that includes invalid data. 6. The method of claim 1, wherein the training data segments are extracted from the one or more portions of the training data stream such that two consecutive data segments have overlapping data portions and non-overlapping data portions, wherein the overlapping data portions are less than a threshold percentage of the length of the training data segments. 7. The method of claim 1, wherein extracting the one or more time series shapelets from the one or more training data segments of the training data segments is performed using a Fast-Shapelet method. 8. The method of claim 1, wherein identifying each training data segment of the training data segments as corresponding to the normal operational state of the first oilfield equipment or the failure state of the first oilfield equipment is performed based on information identifying a portion of the training data stream that is generated by the sensor of first oilfield equipment while the first oilfield equipment was in the failed state. 9. The method of claim 1, wherein the first oilfield equipment and the second oilfield equipment are electric submersible pumps or wherein the first oilfield equipment and the second oilfield equipment are gas compressors. 10. A system for predicting a failure of oilfield equipment based on univariate time series, the system comprising: a data storage device to store and provide a training data stream generated by a sensor of first oilfield equipment; a preprocessor to extract training data segments from one or more portions of a training data stream that include training data generated prior to the first oilfield equipment failing to operate normally, wherein the training data in the one or more portions of the training data streams were sampled at a regular sampling rate, wherein the preprocessor identifies each training data segment of the training data segments as corresponding to a normal operational state of the first oilfield equipment or a failure state of the first oilfield equipment, wherein the failure state of the first oilfield equipment corresponds to the oilfield equipment failing to operate normally; and a processor to generate a shapelet-based decision tree by extracting one or more time series shapelets from one or more training data segments of the training data segments, wherein the preprocessor receives a test data stream generated by a sensor of second oilfield equipment, and wherein the processor determines, based on the shapelet-based decision tree, whether one or more test data segments extracted from the test data stream predict a failure of the second oilfield equipment. 11. The system of claim 10, wherein the preprocessor extracts the one or more test data segments from one or more portions of the test data stream and wherein test data in the one or more portions of the test data stream were sampled at the regular sampling rate. 12. The system of claim 10, wherein the processor determines whether the one or more test data segments predict the failure of the second oilfield equipment based on the shapelet-based decision tree by determining Euclidean distances between each test data segment of the one or more test data segments and the one or more time series shapelets. 13. The system of claim 10, wherein the processor indicates a prediction of the failure of the second oilfield equipment if a ratio of a number of test data segments that predict the failure of the second oilfield equipment processed based on the shapelet-based decision tree to a total number of the one or more test data segments exceeds a threshold. 14. The system of claim 10, wherein the preprocessor extracts the training data segments from the one or more portions of the training data stream by excluding a portion of the training data stream that includes invalid data. 15. The system of claim 10, wherein the first oilfield equipment and the second oilfield equipment are electric submersible pumps and wherein the training data stream and the testing data stream include voltage data, current data, or intake pressure data. 16. A method of predicting a failure of oilfield equipment based on multivariate time series, the method comprising: receiving multiple training data streams generated by multiple sensors of first oilfield equipment, wherein the multiple training data streams are time-wise synchronized with each other; extracting, by a preprocessor, training data segments from a portion of each training data stream of the multiple training data streams, the portion of each training data stream including training sensor data generated prior to the first oilfield equipment failing to operate normally; identifying each training data segment of the multiple training data segments as corresponding to a normal operational state of the first oilfield equipment or a failed state of the first oilfield equipment, wherein the failed state of the first oilfield equipment corresponds to the first oilfield equipment failing to operate normally; selecting a subset of training data streams from the multiple training data streams based on computed feature values of each training data segment of the multiple training data segments and based on identification of each training data segment as corresponding to the normal operational state of the first oilfield equipment or the failed state of the first oilfield equipment, wherein each training data stream of the subset of training data streams is generated by a respective sensor of a subset of the multiple sensors; generating, by a processor, shapelet-based decision trees, wherein generating the shapelet-based decision trees includes extracting time series shapelets from training data segments extracted from the subset of the training data segments; receiving test data streams from a subset of sensors of second oilfield equipment, wherein each sensor of the subset of the sensors of the second oilfield equipment measures same parameter as a respective sensor of the subset of the multiple sensors of the first oilfield equipment; and determining by the processor, based on the shapelet-based decision trees, whether test data segments extracted from the test data streams predict a failure of the second oilfield equipment. 17. The method of claim 16, wherein the computed feature values of each training data segment of the multiple training data segments include two or more of a mean value, a missing data points value, a mean slope, a ratio of measurements, and an exponential decay value. 18. The method of claim 17, selecting the subset of the training data streams from the multiple training data streams includes performing linear classifications of the computed features of each training data segment of the training data segments. 19. The method of claim 16, further comprising indicating a predication of the failure of the second oilfield equipment if a majority of predictions based on the shapelet-based decision trees predict the failure of the second oilfield equipment. 20. The method of claim 16, wherein the first oilfield equipment and the second oilfield equipment are electric submersible pumps or wherein the first oilfield equipment and the second oilfield equipment are gas compressors. 21. A method of predicting a failure of oilfield equipment based on multivariate time series, the method comprising: concatenating, by a preprocessor, cut-size training data segments of multiple training data streams to generate a concatenated training data stream based on an order of a ranking of multiple sensors of first oilfield equipment, wherein the multiple training data streams are received from the multiple sensors of first oilfield equipment and wherein the multiple training data streams are time-wise synchronized with each other; generating, by a processor, a shapelet-based decision tree, wherein generating the shapelet-based decision tree includes extracting one or more time series shapelets from the concatenated training data stream; concatenating cut-size test data segments of test data streams in the order of the ranking of the multiple sensors of the first oilfield equipment to generate a concatenated test data stream, wherein the test data streams are received from sensor of second oilfield equipment; and determining by the processor, based on the shapelet-based decision tree, whether the concatenated test data stream predicts a failure of the second oilfield equipment. 22. The method of claim 21, further comprising: providing multiple training data by the multiple sensors of first oilfield equipment, wherein the multiple training data streams comprise the multiple training data and wherein the multiple training data streams are received by the preprocessor; extracting, by the preprocessor, the training data segments from a portion of each training data stream of the multiple training data streams; and identifying each training data segment of the multiple training data segments as corresponding to a normal operational state of the first oilfield equipment or a failed state of the first oilfield equipment. 23. The method of claim 22, wherein the portion of each training data stream includes training sensor data generated prior to the first oilfield equipment failing to operate normally and wherein the failed state of the first oilfield equipment corresponds to the first oilfield equipment failing to operate normally 24. The method of claim 21, further comprising ranking the multiple sensors of the first oilfield equipment based on the multiple training data streams. 25. The method of claim 21, wherein each sensor of the second oilfield equipment measures same parameter as a respective sensor of the multiple sensors of the first oilfield equipment.