Fetching the paper…
Reading the bibliography…
Deep learning has contributed remarkably to the advancement of time series analysis.
The interpolation of time series by related series
Friedman, M · 1962
Earlier work this paper cites.
Efficient tests for an autoregressive unit root
Elliott, G., Rothenberg, T. J., and Stock, J. H · 1996
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
A neural probabilistic language model
Bengio, Y., Ducharme, R., and Vincent, P · 2000
Earlier work this paper cites.
Lof: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J · 2000
Earlier work this paper cites.
Box and jenkins: time series analysis, forecasting and control
Box, G · 2013
Earlier work this paper cites.
Forecastable component analysis
Goerg, G · 2013
Earlier work this paper cites.
Time series analysis: forecasting and control
Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Flu portal dashboard, 2017
CDC · 2017
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.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Beijing Multi-Site Air Quality
Chen, S · 2019
Earlier work this paper cites.
The ucr time series archive
Dau, H. A., Bagnall, A., Kamgar, K., Yeh, C.-C. M., Zhu, Y., Gharghabi, S., Ratanamahatana, C. A., and Keogh, E · 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., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 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., et al · 2020
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Earlier work this paper cites.
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
Cited alongside, same era.
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
Cited alongside, same era.
Beit: Bert pre-training of image transformers
Bao, H., Dong, L., Piao, S., and Wei, F · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Sdwpf: A dataset for spatial dynamic wind power forecasting challenge at kdd cup 2022
Zhou, J., Lu, X., Xiao, Y., Su, J., Lyu, J., Ma, Y., and Dou, D · 2022
Later among the works it cites.
Residential Power and Battery Data, 2023
Bergmeir, C., Bui, Q., de Nijs, F., and Stuckey, P · 2023
Later among the works it cites.
Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms
Chang, C., Peng, W.-C., and Chen, T.-F · 2023
Later among the works it cites.
Simmtm: A simple pre-training framework for masked time-series modeling
Dong, J., Wu, H., Zhang, H., Zhang, L., Wang, J., and Long, M · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Godahewa, R., Bergmeir, C., Webb, G. I., Hyndman, R. J., and Montero-Manso, P · 2021
Cited alongside, same era.
A machine learning approach for forecasting hierarchical time series
Mancuso, P., Piccialli, V., and Sudoso, A. M · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Cited alongside, same era.
Time series extrinsic regression: Predicting numeric values from time series data
Tan, C. W., Bergmeir, C., Petitjean, F., and Webb, G. I · 2021
Cited alongside, same era.
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.
Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress
Wu, R. and Keogh, E · 2021
Cited alongside, same era.
Anomaly transformer: Time series anomaly detection with association discrepancy
Xu, J., Wu, H., Wang, J., and Long, M · 2021
Cited alongside, same era.
Videogpt: Video generation using vq-vae and transformers
Yan, W., Zhang, Y., Abbeel, P., and Srinivas, A · 2021
Cited alongside, same era.
Dooley, S., Khurana, G. S., Mohapatra, C., Naidu, S., and White, C · 2023
Later among the works it cites.
Buildingsbench: A large-scale dataset of 900k buildings and benchmark for short-term load forecasting
Emami, P., Sahu, A., and Graf, P · 2023
Later among the works it cites.
Libcity: A unified library towards efficient and comprehensive urban spatial-temporal prediction
Jiang, J., Han, C., Jiang, W., Zhao, W. X., and Wang, J · 2023
Later among the works it cites.
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
Later among the works it cites.
Gpt-4 technical report. arxiv 2303.08774
OpenAI, R · 2023
Later among the works it cites.
Lag-llama: Towards foundation models for time series forecasting
Rasul, K., Ashok, A., Williams, A. R., Khorasani, A., Adamopoulos, G., Bhagwatkar, R., Biloš, M., Ghonia, H., Hassen, N. V., Schneider, A., et al · 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., et al · 2023
Later among the works it cites.
Pushing the limits of pre-training for time series forecasting in the cloudops domain
Woo, G., Liu, C., Kumar, A., and Sahoo, 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.
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
Later among the works it cites.
One fits all: Power general time series analysis by pretrained lm
Zhou, T., Niu, P., Wang, X., Sun, L., and Jin, R · 2023
Later among the works it cites.
Chronos: Learning the language of time series
Ansari, A. F., Stella, L., Turkmen, C., Zhang, X., Mercado, P., Shen, H., Shchur, O., Rangapuram, S. S., Arango, S. P., Kapoor, S., et al · 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.
Autotimes: Autoregressive time series forecasters via large language models
Liu, Y., Qin, G., Huang, X., Wang, J., and Long, M · 2024
Closest in time.
Subseasonalclimateusa: A dataset for subseasonal forecasting and benchmarking
Mouatadid, S., Orenstein, P., Flaspohler, G., Oprescu, M., Cohen, J., Wang, F., Knight, S., Geogdzhayeva, M., Levang, S., Fraenkel, E., et al · 2024
Closest in time.
Climatelearn: Benchmarking machine learning for weather and climate modeling
Nguyen, T., Jewik, J., Bansal, H., Sharma, P., and Grover, A · 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.