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Time series analysis is essential for comprehending the complexities inherent in various realworld systems and applications.
Time series forecasting using holt-winters exponential smoothing
Kalekar, P. S. et al · 2004
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The problem of concept drift: definitions and related work
Tsymbal, A · 2004
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Analysis of financial time series
Tsay, R. S · 2005
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Introduction to statistical time series
Fuller, W. A · 2009
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A public domain dataset for human activity recognition using smartphones
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J. L., et al · 2013
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Deep learning for time-series analysis
Gamboa, J. C. B · 2017
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Arima models
Shumway, R. H., Stoffer, D. S., Shumway, R. H., and Stoffer, D. S · 2017
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Forecasting traffic congestion using arima modeling
Alghamdi, T., Elgazzar, K., Bayoumi, M., Sharaf, T., and Shah, S · 2019
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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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Time series analysis
Hamilton, J. D · 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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Ccnet: Extracting high quality monolingual datasets from web crawl data
Wenzek, G., Lachaux, M.-A., Conneau, A., Chaudhary, V., Guzmán, F., Joulin, A., and Grave, É · 2020
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Time series data augmentation for deep learning: A survey
Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., and Xu, H · 2021
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Constitutional ai: Harmlessness from ai feedback
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., et al · 2022
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Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Huang, W., Abbeel, P., Pathak, D., and Mordatch, I · 2022
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Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
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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 · 2022
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Multitask prompted training enables zero-shot task generalization
Victor, S., Albert, W., Colin, R., Stephen, B., Lintang, S., Zaid, A., Antoine, C., Arnaud, S., Arun, R., Manan, D., et al · 2022
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Robust time series analysis and applications: An industrial perspective
Wen, Q., Yang, L., Zhou, T., and Sun, L · 2022
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
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Graph of thoughts: Solving elaborate problems with large language models
Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Gianinazzi, L., Gajda, J., Lehmann, T., Podstawski, M., Niewiadomski, H., Nyczyk, P., et al · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al · 2023
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Tempo: Prompt-based generative pre-trained transformer for time series forecasting
Cao, D., Jia, F., Arik, S. O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y · 2023
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Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms
Chang, C., Peng, W.-C., and Chen, T.-F · 2023
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Chatterjee, S., Mitra, B., and Chakraborty, S · 2023
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Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al · 2023
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Llm powered sim-to-real transfer for traffic signal control
Da, L., Gao, M., Mei, H., and Wei, H · 2023
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Tic-clip: Continual training of clip models
Garg, S., Farajtabar, M., Pouransari, H., Vemulapalli, R., Mehta, S., Tuzel, O., Shankar, V., and Faghri, F · 2023
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Garza, A. and Mergenthaler-Canseco, M · 2023
Cited alongside, same era.
Openagi: When llm meets domain experts
Ge, Y., Hua, W., Mei, K., Tan, J., Xu, S., Li, Z., Zhang, Y., et al · 2023
Cited alongside, same era.
Spatio-temporal storytelling? leveraging generative models for semantic trajectory analysis
Ghosh, S., Sengupta, S., and Mitra, P · 2023
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.
The false promise of imitating proprietary llms
Gudibande, A., Wallace, E., Snell, C., Geng, X., Liu, H., Abbeel, P., Levine, S., and Song, D · 2023
Cited alongside, same era.
When urban region profiling meets large language models
Yan, Y., Wen, H., Zhong, S., Chen, W., Chen, H., Wen, Q., Zimmermann, R., and Liang, Y · 2023
Later among the works it cites.
Toward a foundation model for time series data
Yeh, C.-C. M., Dai, X., Chen, H., Zheng, Y., Fan, Y., Der, A., Lai, V., Zhuang, Z., Wang, J., Wang, L., et al · 2023
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Harnessing LLMs for temporal data - a study on explainable financial time series forecasting
Yu, X., Chen, Z., and Lu, Y · 2023
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Insight miner: A time series analysis dataset for cross-domain alignment with natural language
Zhang, Y., Zhang, Y., Zheng, M., Chen, K., Gao, C., Ge, R., Teng, S., Jelloul, A., Rao, J., Guo, X., et al · 2023
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A survey of large language models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al · 2023
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Kamarthi, H. and Prakash, B. A · 2023
Cited alongside, same era.
Large language models as traffic signal control agents: Capacity and opportunity
Lai, S., Xu, Z., Zhang, W., Liu, H., and Xiong, H · 2023
Cited alongside, same era.
Aligning text-to-image models using human feedback
Lee, K., Liu, H., Ryu, M., Watkins, O., Du, Y., Boutilier, C., Abbeel, P., Ghavamzadeh, M., and Gu, S. S · 2023
Cited alongside, same era.
Helma: A large-scale hallucination evaluation benchmark for large language models
Li, J., Cheng, X., Zhao, W. X., Nie, J.-Y., and Wen, J.-R · 2023
Cited alongside, same era.
Exploring large language models for human mobility prediction under public events
Liang, Y., Liu, Y., Wang, X., and Zhao, Z · 2023
Cited alongside, same era.
Biosignal copilot: Leveraging the power of llms in drafting reports for biomedical signals
Liu, C., Ma, Y., Kothur, K., Nikpour, A., and Kavehei, O · 2023
Cited alongside, same era.
Can chatgpt forecast stock price movements? return predictability and large language models
Lopez-Lira, A. and Tang, Y · 2023
Cited alongside, same era.
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Exploring ai ethics of chatgpt: A diagnostic analysis
Zhuo, T. Y., Huang, Y., Chen, C., and Xing, Z · 2023
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Exploring the potential of large language models (llms) in learning on graphs
Chen, Z., Mao, H., Li, H., Jin, W., Wen, H., Wei, X., Wang, S., Yin, D., Fan, W., Liu, H., et al · 2024
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Sociodojo: Building lifelong analytical agents with real-world text and time series
Cheng, J. and Chin, P · 2024
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Exploring large language model based intelligent agents: Definitions, methods, and prospects
Cheng, Y., Zhang, C., Zhang, Z., Meng, X., Hong, S., Li, W., Wang, Z., Wang, Z., Yin, F., Zhao, J., et al · 2024
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Open-ti: Open traffic intelligence with augmented language model
Da, L., Liou, K., Chen, T., Zhou, X., Luo, X., Yang, Y., and Wei, H · 2024
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A decoder-only foundation model for time-series forecasting
Das, A., Kong, W., Sen, R., and Zhou, Y · 2024
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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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Can large language models beat wall street? unveiling the potential of ai in stock selection
Fatouros, G., Metaxas, K., Soldatos, J., and Kyriazis, D · 2024
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Anomalygpt: Detecting industrial anomalies using large vision-language models
Gu, Z., Zhu, B., Zhu, G., Chen, Y., Tang, M., and Wang, J · 2024
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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 · 2024
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Health-llm: Large language models for health prediction via wearable sensor data
Kim, Y., Xu, X., McDuff, D., Breazeal, C., and Park, H. W · 2024
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Frozen language model helps ecg zero-shot learning
Li, J., Liu, C., Cheng, S., Arcucci, R., and Hong, S · 2024
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Foundation models for time series analysis: A tutorial and survey
Liang, Y., Wen, H., Nie, Y., Jiang, Y., Jin, M., Song, D., Pan, S., and Wen, Q · 2024
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Ai transparency in the age of llms: A human-centered research roadmap
Liao, Q. V. and Vaughan, J. W · 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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Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al · 2024
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Ecg-qa: A comprehensive question answering dataset combined with electrocardiogram
Oh, J., Lee, G., Bae, S., Kwon, J.-m., and Choi, E · 2024
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Toolformer: Language models can teach themselves to use tools
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., and Scialom, T · 2024
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Test: Text prototype aligned embedding to activate llm’s ability for time series
Sun, C., Li, Y., Li, H., and Hong, S · 2024
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A survey on large language model based autonomous agents
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., et al · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., and Narasimhan, K · 2024
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Lamm: Language-assisted multi-modal instruction-tuning dataset, framework, and benchmark
Yin, Z., Wang, J., Cao, J., Shi, Z., Liu, D., Li, M., Huang, X., Wang, Z., Sheng, L., Bai, L., et al · 2024
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