Timenet time series classification series#
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The UCR time series classication archive.(). Using dynamic time warping to find patterns in time series // Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining. Weighted dynamic time warping for time series classification. Semi-supervised time series classication // Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Data Mining and Knowledge Discovery, 2018, 33, 378- 412. A review on distance based time series classification. IEEE Transactions on Artificial Intelligence, 2020, 1 (1): 47- 61.ĪBANDA A, MORI U, LOZANO J A. Approaches and applications of early classification of time series: A review. A literature survey of early time series classification and deep learning // SamI40 Workshop at i-KNOW’16. Early classification of time series using multi-objective optimization techniques. Forecasting stock indices: A comparison of classification and level estimation models. A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting. Key words: early time series classification, time series classifier, minimum prediction length, shapelet, machine learning Lastly, we explore future research directions pertinent to ETSC. Next, we review public time-series datasets in fintech and commonly used performance evaluation criteria. There are pivotal technologies, advantages, and disadvantages of the representative ETSC methods in separate frameworks. First, this paper summarizes the common classifiers for time-series data and reviews the current research progress on minimum prediction length-based, shapelet-based, and model-based ETSC frameworks. ETSC, in particular, plays a critical role in fintech. Early time series classification (ETSC) aims to classify time-series data with the highest level of accuracy and smallest possible size. With the increasing popularity of sensors, time-series data have attracted significant attention.
Timenet time series classification archive#
For several publicly available datasets from UCR TSC Archive and an industrial telematics sensor data from vehicles, we observe that a classifier learned over the TimeNet embeddings yields significantly better performance compared to (i) a classifier learned over the embeddings given by a domain-specific RNN, as well as (ii) a nearest neighbor classifier based on Dynamic Time Warping.传感器技术的普及使得时间序列数据受到人们越来越多的关注. The representations or embeddings given by a pre-trained TimeNet are found to be useful for time series classification (TSC). Once trained, TimeNet can be used as a generic off-the-shelf feature extractor for time series. Rather than relying on data from the problem domain, TimeNet attempts to generalize time series representation across domains by ingesting time series from several domains simultaneously. Inspired by the tremendous success of deep Convolutional Neural Networks as generic feature extractors for images, we propose TimeNet: a deep recurrent neural network (RNN) trained on diverse time series in an unsupervised manner using sequence to sequence (seq2seq) models to extract features from time series.
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