Fetching the paper…
Reading the bibliography…
This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resources.
Time series analysis
Madsen, H · 2007
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Bai, S., Kolter, J. Z., and Koltun, V · 2018
Earlier work this paper cites.
Unsupervised scalable representation learning for multivariate time series
Franceschi, J.-Y., Dieuleveut, A., and Jaggi, M · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Wu, H., Xu, J., Wang, J., and Long, M · 2021
Earlier work this paper cites.
Informer: Beyond efficient transformer for long sequence time-series forecasting
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W · 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.
Micn: Multi-scale local and global context modeling for long-term series forecasting
Wang, H., Peng, J., Huang, F., Wang, J., Chen, J., and Xiao, Y · 2022
Cited alongside, same era.
Transformers in time series: A survey
Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., and Sun, L · 2022
Cited alongside, same era.
Less is more: Fast multivariate time series forecasting with light sampling-oriented mlp structures
Zhang, T., Zhang, Y., Cao, W., Bian, J., Yi, X., Zheng, S., and Li, J · 2022
Cited alongside, same era.
A time series is worth 64 words: Long-term forecasting with transformers
Nie, Y., H. Nguyen, N., Sinthong, P., and Kalagnanam, J · 2023
Later among the works it cites.
Timesnet: Temporal 2d-variation modeling for general time series analysis
Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., and Long, M · 2023
Later among the works it cites.
Promptcast: A new prompt-based learning paradigm for time series forecasting
Xue, H. and Salim, F. D · 2023
Later among the works it cites.
Are transformers effective for time series forecasting?
Zeng, A., Chen, M., Zhang, L., and Xu, Q · 2023
Later among the works it cites.
Robust recurrent neural networks for time series forecasting
Zhang, X., Zhong, C., Zhang, J., Wang, T., and Ng, W. W · 2023
Later among the works it cites.
Llm4ts: Aligning pre-trained llms as data-efficient time-series forecasters, 2024
Chang, C., Wang, W.-Y., Peng, W.-C., and Chen, T.-F · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nhits: Neural hierarchical interpolation for time series forecasting
Challu, C., Olivares, K. G., Oreshkin, B. N., Ramirez, F. G., Canseco, M. M., and Dubrawski, A · 2023
Cited alongside, same era.
Long-term forecasting with tide: Time-series dense encoder
Das, A., Kong, W., Leach, A., Mathur, S., Sen, R., and Yu, R · 2023
Cited alongside, same era.
Tsmixer: Lightweight mlp-mixer model for multivariate time series forecasting
Ekambaram, V., Jati, A., Nguyen, N., Sinthong, P., and Kalagnanam, J · 2023
Cited alongside, same era.
He, X., Li, Y., Tan, J., Wu, B., and Li, F · 2023
Cited alongside, same era.
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., et al · 2023
Cited alongside, same era.
Hdmixer: Hierarchical dependency with extendable patch for multivariate time series forecasting
Huang, Q., Shen, L., Zhang, R., Cheng, J., Ding, S., Zhou, Z., and Wang, Y
Cited in the paper.
Crossgnn: Confronting noisy multivariate time series via cross interaction refinement
Huang, Q., Shen, L., Zhang, R., Ding, S., Wang, B., Zhou, Z., and Wang, Y
Cited in the paper.
Closest in time.
The bigger the better? rethinking the effective model scale in long-term time series forecasting
Deng, J., Song, X., Tsang, I. W., and Xiong, H · 2024
Closest in time.
The capacity and robustness trade-off: Revisiting the channel independent strategy for multivariate time series forecasting
Han, L., Ye, H.-J., and Zhan, D.-C · 2024
Closest in time.
Tfb: Towards comprehensive and fair benchmarking of time series forecasting methods
Qiu, X., Hu, J., Zhou, L., Wu, X., Du, J., Zhang, B., Guo, C., Zhou, A., Jensen, C. S., Sheng, Z., et al · 2024
Closest in time.
Fits: Modeling time series with 10 k 10k parameters
Xu, Z., Zeng, A., and Xu, Q · 2024
Closest in time.