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Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models.
Freeway performance measurement system: mining loop detector data
Chen, C., Petty, K., Skabardonis, A., Varaiya, P., and Jia, Z · 2001
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Another look at measures of forecast accuracy
Hyndman, R. J. and Koehler, A. B · 2006
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Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E · 2007
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Errors on percentage errors, 4 2014
Hyndman, R. J · 2014
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Walmart recruiting - store sales forecasting, 2014
Walmart Competition Admin, W. C · 2014
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ElectricityLoadDiagrams20112014
Trindade, A · 2015
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Forecasting fine-grained air quality based on big data
Zheng, Y., Yi, X., Li, M., Li, R., Shan, Z., Chang, E., and Li, T · 2015
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Temporal regularized matrix factorization for high-dimensional time series prediction
Yu, H.-F., Rao, N., and Dhillon, I. S · 2016
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Flu portal dashboard, 2017
CDC · 2017
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Forecasting: principles and practice
Hyndman, R. J. and Athanasopoulos, G · 2018
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Modeling long-and short-term temporal patterns with deep neural networks
Lai, G., Chang, W.-C., Yang, Y., and Liu, H · 2018
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Project tycho 2.0: a repository to improve the integration and reuse of data for global population health
van Panhuis, W. G., Cross, A., and Burke, D. S · 2018
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Beijing Multi-Site Air-Quality Data
Chen, S · 2019
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Root mean square layer normalization
Zhang, B. and Sennrich, R · 2019
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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
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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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
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Query-key normalization for transformers
Henry, A., Dachapally, P. R., Pawar, S. S., and Chen, Y · 2020
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
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The m4 competition: 100,000 time series and 61 forecasting methods
Makridakis, S., Spiliotis, E., and Assimakopoulos, V · 2020
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N-beats: Neural basis expansion analysis for interpretable time series forecasting
Oreshkin, B. N., Carpov, D., Chapados, N., and Bengio, Y · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
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Deepar: Probabilistic forecasting with autoregressive recurrent networks
Salinas, D., Flunkert, V., Gasthaus, J., and Januschowski, T · 2020
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Glu variants improve transformer
Shazeer, N · 2020
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On layer normalization in the transformer architecture
Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., and Liu, T · 2020
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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
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Monash time series forecasting archive
Godahewa, R. W., Bergmeir, C., Webb, G. I., Hyndman, R., and Montero-Manso, P · 2021
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Temporal fusion transformers for interpretable multi-horizon time series forecasting
Lim, B., Arık, S. Ö., Loeff, N., and Pfister, T · 2021
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Garza, A. and Mergenthaler-Canseco, M · 2023
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Large language models are zero-shot time series forecasters
Gruver, N., Finzi, M. A., Qiu, S., and Wilson, A. G · 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., et al · 2023
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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
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SubseasonalclimateUSA: A dataset for subseasonal forecasting and benchmarking
Mouatadid, S., Orenstein, P., Flaspohler, G. E., Oprescu, M., Cohen, J., Wang, F., Knight, S. E., Geogdzhayeva, M., Levang, S. J., Fraenkel, E., and Mackey, L · 2023
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A machine learning approach for forecasting hierarchical time series
Mancuso, P., Piccialli, V., and Sudoso, A. M · 2021
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Wu, H., Xu, J., Wang, J., and Long, M · 2021
Cited alongside, same era.
A transformer-based framework for multivariate time series representation learning
Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., and Eickhoff, C · 2021
Cited alongside, same era.
On the benefits of maximum likelihood estimation for regression and forecasting
Awasthi, P., Das, A., Sen, R., and Suresh, A. T · 2022
Cited alongside, same era.
StatsForecast: Lightning fast forecasting with statistical and econometric models
Garza, F., Canseco, M. M., Challú, C., and Olivares, K. G · 2022
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 · 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
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Climatelearn: Benchmarking machine learning for weather and climate modeling
Nguyen, T., Jewik, J. K., Bansal, H., Sharma, P., and Grover, A · 2023
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A time series is worth 64 words: Long-term forecasting with transformers
Nie, Y., Nguyen, N. H., Sinthong, P., and Kalagnanam, J · 2023
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Lag-llama: Towards foundation models for time series forecasting, 2023
Rasul, K., Ashok, A., Williams, A. R., Khorasani, A., Adamopoulos, G., Bhagwatkar, R., Biloš, M., Ghonia, H., Hassen, N. V., Schneider, A., Garg, S., Drouin, A., Chapados, N., Nevmyvaka, Y., and Rish, I · 2023
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arrow: Integration to ’Apache’ ’Arrow’ , 2023
Richardson, N., Cook, I., Crane, N., Dunnington, D., François, R., Keane, J., Moldovan-Grünfeld, D., Ooms, J., Wujciak-Jens, J., and Apache Arrow · 2023
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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
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Cross-frequency time series meta-forecasting
Van Ness, M., Shen, H., Wang, H., Jin, X., Maddix, D. C., and Gopalswamy, K · 2023
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Pushing the limits of pre-training for time series forecasting in the cloudops domain
Woo, G., Liu, C., Kumar, A., and Sahoo, D · 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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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., Liang, Y., Pang, G., Song, D., et al · 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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Ekambaram, V., Jati, A., Nguyen, N. H., Dayama, P., Reddy, C., Gifford, W. M., and Kalagnanam, J · 2024
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Latent diffusion transformer for probabilistic time series forecasting
Feng, S., Miao, C., Zhang, Z., and Zhao, P · 2024
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Unitime: A language-empowered unified model for cross-domain time series forecasting
Liu, X., Hu, J., Li, Y., Diao, S., Liang, Y., Hooi, B., and Zimmermann, R · 2024
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Roformer: Enhanced transformer with rotary position embedding
Su, J., Ahmed, M., Lu, Y., Pan, S., Bo, W., and Liu, Y · 2024
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Moirai — Wikipedia, the free encyclopedia, 2024
Wikipedia contributors · 2024
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