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Time series data, characterized by its intrinsic long and short-range dependencies, poses a unique challenge across analytical applications.
Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E · 2000
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The impact of the mit-bih arrhythmia database
Moody, G. and Mark, R · 2001
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Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., and Moore, S. A · 2011
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A public domain dataset for human activity recognition using smartphones
Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J. L · 2013
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Smart devices are different: Assessing and mitigatingmobile sensing heterogeneities for activity recognition
Stisen, A., Blunck, H., Bhattacharya, S., Prentow, T. S., Kjærgaard, M. B., Dey, A., Sonne, T., and Jensen, M. M · 2015
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Swat: A water treatment testbed for research and training on ics security
Mathur, A. P. and Tippenhauer, N. O · 2016
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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The uea multivariate time series classification archive, 2018, 2018
Bagnall, A., Dau, H. A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., and Keogh, E · 2018
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Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding
Hundman, K., Constantinou, V., Laporte, C., Colwell, I., and Soderstrom, T · 2018
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The ucr time series archive, 2019
Dau, H. A., Bagnall, A., Kamgar, K., Yeh, C.-C. M., Zhu, Y., Gharghabi, S., Ratanamahatana, C. A., and Keogh, E · 2019
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Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting
Li, S., Jin, X., Xuan, Y., Zhou, X., Chen, W., Wang, Y.-X., and Yan, X · 2019
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Wavelet transform application for/in non-stationary time-series analysis: A review
Rhif, M., Ben Abbes, A., Farah, I. R., Martínez, B., and Sang, Y · 2019
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Robust anomaly detection for multivariate time series through stochastic recurrent neural network
Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., and Pei, D · 2019
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ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels
Dempster, A., Petitjean, F., and Webb, G. I · 2020
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Reformer: The efficient transformer
Kitaev, N., Kaiser, L., and Levskaya, A · 2020
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Practical approach to asynchronous multivariate time series anomaly detection and localization
Abdulaal, A., Liu, Z., and Lancewicki, T · 2021
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Convit: Improving vision transformers with soft convolutional inductive biases
D’Ascoli, S., Touvron, H., Leavitt, M. L., Morcos, A. S., Biroli, G., and Sagun, L · 2021
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Time-series representation learning via temporal and contextual contrasting
Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C. K., Li, X., and Guan, C · 2021
Cited alongside, same era.
Reversible instance normalization for accurate time-series forecasting against distribution shift
Kim, T., Kim, J., Tae, Y., Park, C., Choi, J.-H., and Choo, J · 2021
Cited alongside, same era.
Informer: Beyond efficient transformer for long sequence time-series forecasting
Li, J., Hui, X., and Zhang, W · 2021
Cited alongside, same era.
Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting
Liu, S., Yu, H., Liao, C., Li, J., Lin, W., Liu, A. X., and Dustdar, S · 2021
Cited alongside, same era.
FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., and Jin, R · 2022
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Tsmixer: Lightweight mlp-mixer model for multivariate time series forecasting
Ekambaram, V., Jati, A., Nguyen, N., Sinthong, P., and Kalagnanam, J · 2023
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Revisiting long-term time series forecasting: An investigation on linear mapping
Li, Z., Qi, S., Li, Y., and Xu, Z · 2023
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Wftnet: Exploiting global and local periodicity in long-term time series forecasting
Liu, P., Wu, B., Li, N., Dai, T., Lei, F., Bao, J., Jiang, Y., and Xia, S.-T · 2023
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MHCCL: masked hierarchical cluster-wise contrastive learning for multivariate time series
Meng, Q., Qian, H., Liu, Y., Cui, L., Xu, Y., and Shen, Z · 2023
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Global filter networks for image classification
Rao, Y., Zhao, W., Zhu, Z., Lu, J., and Zhou, J · 2021
Cited alongside, same era.
Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
Cited alongside, same era.
Uniformer: Unified transformer for efficient spatial-temporal representation learning
Li, K., Wang, Y., Peng, G., Song, G., Liu, Y., Li, H., and Qiao, Y · 2022
Cited alongside, same era.
Scinet: time series modeling and forecasting with sample convolution and interaction
Liu, M., Zeng, A., Chen, M., Xu, Z., Lai, Q., Ma, L., and Xu, Q · 2022
Cited alongside, same era.
T-wavenet: A tree-structured wavelet neural network for time series signal analysis
LIU, M., Zeng, A., LAI, Q., Gao, R., Li, M., Qin, J., and Xu, Q · 2022
Cited alongside, same era.
Non-stationary transformers: Rethinking the stationarity in time series forecasting
Liu, Y., Wu, H., Wang, J., and Long, M · 2022
Cited alongside, same era.
Etsformer: Exponential smoothing transformers for time-series forecasting
Woo, G., Liu, C., Sahoo, D., Kumar, A., and Hoi, S · 2022
Cited alongside, same era.
A time series is worth 64 words: Long-term forecasting with transformers
Nie, Y., Nguyen, N. H., Sinthong, P., and Kalagnanam, J · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G · 2023
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Transformers in time series: A survey
Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., and Sun, L · 2023
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Timesnet: Temporal 2d-variation modeling for general time series analysis
Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., and Long, M · 2023
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Are transformers effective for time series forecasting?
Zeng, A., Chen, M., Zhang, L., and Xu, Q · 2023
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Temporal convolutional explorer helps understand 1d-cnn’s learning behavior in time series classification from frequency domain
Zhang, J., Feng, L., He, Y., Wu, Y., and Dong, Y · 2023
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Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Zhang, Y. and Yan, J · 2023
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One fits all: Power general time series analysis by pretrained LM
Zhou, T., Niu, P., Wang, X., Sun, L., and Jin, R · 2023
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Time-LLM: Time series forecasting by reprogramming large language models
Jin, M., Wang, S., Ma, L., Chu, Z., Zhang, J. Y., Shi, X., Chen, P.-Y., Liang, Y., Li, Y.-F., Pan, S., and Wen, Q · 2024
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itransformer: Inverted transformers are effective for time series forecasting
Liu, Y., Hu, T., Zhang, H., Wu, H., Wang, S., Ma, L., and Long, M · 2024
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