Fetching the paper…
Reading the bibliography…
Foundation models have emerged as a promising approach in time series forecasting (TSF).
Language models are few-shot learners, 2020
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2005
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Classification of time-series images using deep convolutional neural networks
Hatami, N., Gavet, Y., and Debayle, J · 2018
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Earlier work this paper cites.
GluonTS: Probabilistic and Neural Time Series Modeling in Python
Alexandrov, A., Benidis, K., Bohlke-Schneider, M., Flunkert, V., Gasthaus, J., Januschowski, T., Maddix, D. C., Rangapuram, S., Salinas, D., Schulz, J., Stella, L., Türkmen, A. C., and Wang, Y · 2020
Earlier work this paper cites.
Forecasting with time series imaging
Li, X., Kang, Y., and Li, F · 2020
Earlier work this paper cites.
On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al · 2021
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., Uszkoreit, J., and Houlsby, N · 2021
Earlier work this paper cites.
Monash time series forecasting archive
Godahewa, R. W., Bergmeir, C., Webb, G. I., Hyndman, R., and Montero-Manso, P · 2021
Earlier work this paper cites.
HuBERT: Self-supervised speech representation learning by masked prediction of hidden units
Hsu, W.-N., Bolte, B., Tsai, Y.-H. H., Lakhotia, K., Salakhutdinov, R., and Mohamed, A · 2021
Earlier work this paper cites.
Exploiting cloze-questions for few-shot text classification and natural language inference
Schick, T. and Schütze, H · 2021
Earlier work this paper cites.
Visual time series forecasting: an image-driven approach
Sood, S., Zeng, Z., Cohen, N., Balch, T., and Veloso, M · 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.
A transformer-based framework for multivariate time series representation learning
Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., and Eickhoff, C · 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
Earlier work this paper cites.
BEit: BERT pre-training of image transformers
Bao, H., Dong, L., Piao, S., and Wei, F · 2022
Earlier work this paper cites.
Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
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 · 2022
Earlier work this paper cites.
Non-stationary transformers: Exploring the stationarity in time series forecasting, 2022
Liu, Y., Wu, H., Wang, J., and Long, M · 2022
Earlier work this paper cites.
A time series is worth 64 words: Long-term forecasting with transformers
Nie, Y., Nguyen, N. H., Sinthong, P., and Kalagnanam, J · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Cited alongside, same era.
Resolution-robust large mask inpainting with fourier convolutions
Suvorov, R., Logacheva, E., Mashikhin, A., Remizova, A., Ashukha, A., Silvestrov, A., Kong, N., Goka, H., Park, K., and Lempitsky, V · 2022
Cited alongside, same era.
Ts2vec: Towards universal representation of time series
Yue, Z., Wang, Y., Duan, J., Yang, T., Huang, C., Tong, Y., and Xu, B · 2022
Cited alongside, same era.
Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Zaken, E. B., Goldberg, Y., and Ravfogel, S · 2022
Cited alongside, same era.
Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Calibration of time-series forecasting: Detecting and adapting context-driven distribution shift
Chen, M., Shen, L., Fu, H., Li, Z., Sun, J., and Liu, C · 2024
Closest in time.
A decoder-only foundation model for time-series forecasting
Das, A., Kong, W., Sen, R., and Zhou, Y · 2024
Closest in time.
Timesiam: A pre-training framework for siamese time-series modeling
Dong, J., Wu, H., Wang, Y., Qiu, Y.-Z., Zhang, L., Wang, J., and Long, M · 2024
Closest in time.
Tiny time mixers (ttms): Fast pre-trained models for enhanced zero/few-shot forecasting of multivariate time series
Ekambaram, V., Jati, A., Dayama, P., Mukherjee, S., Nguyen, N., Gifford, W. M., Reddy, C., and Kalagnanam, J · 2024
Closest in time.
Feng, C., Huang, L., and Krompass, D · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., and Jin, R · 2022
Cited alongside, same era.
Large language models are zero-shot time series forecasters
Gruver, N., Finzi, M., Qiu, S., and Wilson, A. G · 2023
Cited alongside, same era.
A survey on time-series pre-trained models
Ma, Q., Liu, Z., Zheng, Z., Huang, Z., Zhu, S., Yu, Z., and Kwok, J. T · 2023
Cited alongside, same era.
Scalable diffusion models with transformers
Peebles, W. and Xie, S · 2023
Cited alongside, same era.
Image-based time series forecasting: A deep convolutional neural network approach
Semenoglou, A.-A., Spiliotis, E., and Assimakopoulos, V · 2023
Cited alongside, same era.
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., et al · 2023
Cited alongside, same era.
Leveraging vision-language models for granular market change prediction
Wimmer, C. and Rekabsaz, N · 2023
Cited alongside, same era.
Are synthetic time-series data really not as good as real data?, 2024
Fu, F., Chen, J., Zhang, J., Yang, C., Ma, L., and Yang, Y · 2024
Closest in time.
Moment: A family of open time-series foundation models
Goswami, M., Szafer, K., Choudhry, A., Cai, Y., Li, S., and Dubrawski, A · 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.
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 · 2024
Closest in time.
Time series as images: Vision transformer for irregularly sampled time series
Li, Z., Li, S., and Yan, X · 2024
Closest in time.
Sparsetsf: Modeling long-term time series forecasting with 1k parameters
Lin, S., Lin, W., Wu, W., Chen, H., and Yang, J · 2024
Closest in time.
Timer: Generative pre-trained transformers are large time series models
Liu, Y., Zhang, H., Li, C., Huang, X., Wang, J., and Long, M · 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., and Yang, B · 2024
Closest in time.
Time-moe: Billion-scale time series foundation models with mixture of experts
Shi, X., Wang, S., Nie, Y., Li, D., Ye, Z., Wen, Q., and Jin, M · 2024
Closest in time.
Are language models actually useful for time series forecasting?
Tan, M., Merrill, M. A., Gupta, V., Althoff, T., and Hartvigsen, T · 2024
Closest in time.
Unified training of universal time series forecasting transformers
Woo, G., Liu, C., Kumar, A., Xiong, C., Savarese, S., and Sahoo, D · 2024
Closest in time.
Vitime: A visual intelligence-based foundation model for time series forecasting
Yang, L., Wang, Y., Fan, X., Cohen, I., Zhao, Y., and Zhang, Z · 2024
Closest in time.
Self-supervised learning for time series analysis: Taxonomy, progress, and prospects
Zhang, K., Wen, Q., Zhang, C., Cai, R., Jin, M., Liu, Y., Zhang, J. Y., Liang, Y., Pang, G., Song, D., et al · 2024
Closest in time.
Calf: Aligning llms for time series forecasting via cross-modal fine-tuning
Liu, P., Guo, H., Dai, T., Li, N., Bao, J., Ren, X., Jiang, Y., and Xia, S.-T · 2025
Closest in time.
CBramod: A criss-cross brain foundation model for EEG decoding
Wang, J., Zhao, S., Luo, Z., Zhou, Y., Jiang, H., Li, S., Li, T., and Pan, G · 2025
Closest in time.