Abstract
Long Sequence Time-series Forecasting (LSTF) is a fundamental problem with wide real-world applications. However, missing data in time series is ubiquitous, posing significant challenges and affecting the accuracy of long sequence time-series forecasting. To address this issue, we propose a comprehensive framework Time-series Imputation transformer, namely TIformer, for imputing missing data and subsequently conducting time series forecasting. There are two major modules of TIformer, the missing data imputation module and the time series forecasting module. In the first module, we employ data imputation methods to improve the data quality for downstream forecasting. In the second stage, we employ the transformer-based model for long-term time series prediction, which leverages the frequency information on time series. TIformer is an effective work on improving time series forecasting performance on scarce data. Through experiments on various datasets, we have demonstrated the powerful effectiveness of our method in improving prediction accuracy.
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References
Yang, P., Wang, H., Zhang, Y., Qin, L., Zhang, W., Lin, X.: T3s: effective representation learning for trajectory similarity computation. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 2183–2188. IEEE (2021)
Yang, P., Wang, H., Lian, D., Zhang, Y., Qin, L., Zhang, W.: TMN: trajectory matching networks for predicting similarity. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 1700–1713. IEEE (2022)
Zhu, Y., Shasha, D.E.: StatStream: statistical monitoring of thousands of data streams in real time. In: VLDB, pp. 358–369 (2002)
Matsubara, Y., Sakurai, Y., van Panhuis, W.G., Faloutsos, C.: FUNNEL: automatic mining of spatially coevolving epidemics. In: ACM SIGKDD, pp. 105–114 (2014)
Alabadla, M., et al.: Systematic review of using machine learning in imputing missing values. IEEE Access 10, 44483–44502 (2022)
Liu, H., Wang, Y., Chen, W.G.: Three-step imputation of missing values in condition monitoring datasets. IET Gener. Transm. Distrib. 14(16), 3288–3300 (2020)
Liu, H., et al.: A nonlinear regression application via machine learning techniques for geomagnetic data reconstruction processing. IEEE Trans. Geosci. Remote Sens. 57(1), 128–140 (2019)
Hamzah, F.B., Mohd Hamzah, F., Mohd Razali, S.F., Jaafar, O., Abdul Jamil, N.: Imputation methods for recovering streamflow observation: a methodological review. Cogent Environ. Sci. 6(1) (2020)
Avanzi, F., Zheng, Z., Coogan, A., Rice, R., Akella, R., Conklin, M.H.: Gap-filling snow-depth time-series with Kalman filtering-smoothing and expectation maximization: proof of concept using spatially dense wireless-sensor-network data. Cold Reg. Sci. Technol. 175, 103066 (2020)
Kok, I., Ozdemir, S.: DeepMDP: a novel deep-learning based missing data prediction protocol for IoT. IEEE Internet Things J. 8(1), 232–243 (2021)
Li, D., Li, L., Li, X., Ke, Z., Hu, Q.: Smoothed LSTM-AE: a spatio-temporal deep model for multiple time-series missing imputation. Neurocomputing 411, 351–363 (2020)
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)
Yoon, J., Jordon, J., Schaar, M.: GAIN: missing data imputation using generative adversarial nets. In: ICML, pp. 5675–5684 (2018)
Allen, A., Li, W.: Generative adversarial denoising autoencoder for face completion (2016)
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: FEDformer: frequency enhanced decomposed transformer for long-term series forecasting. In: ICML (2022)
Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976)
Xia, J., et al.: Adjusted weight voting algorithm for random forests in handling missing values. Pattern Recogn. 69(1), 52–60 (2017)
Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data, 2nd edn. (2019)
Mellenbergh, G.J.: Counteracting Methodological Errors in Behavioral Research (2019)
Vaswani, A., et al.: Attention is all you need. In: Neural Information Processing Systems (2017)
Zhou, H., et al.: Informer: beyond efficient transformer forlong sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021)
Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. In: Neural Information Processing Systems, vol. 34, pp. 22419–22430 (2021)
Zhang, Y., Yan, J.: Crossformer: transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: ICLR (2023)
Nie, Y., Nguyen, N.H., Sinthong, P., Kalagnanam, J.: A time series is worth 64 words: long-term forecasting with transformers. In: International Conference on Learning Representations (2023)
Liu, Y., et al.: itransformer: inverted transformers are effective for time series forecasting. In: The Twelfth International Conference on Learning Representations (2024)
Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 11121–11128 (2023)
Das, A., Kong, W., Leach, A., Mathur, S.K., Sen, R., Yu, R.: Long-term forecasting with TiDE: timeseries dense encoder. Trans. Mach. Learn. Res. (2023). ISSN: 2835-8856
Wang, H., Peng, J., Huang, F., Wang, J., Chen, J., Xiao, Y.: MICN: multi-scale local and global context modeling for long-term series forecasting. In: The Eleventh International Conference on Learning Representations (2022)
Liu, M., et al.: SCINet: time series modeling and forecasting with sample convolution and interaction. In: Neural Information Processing Systems, vol. 35, pp. 5816–5828 (2022)
Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: TimesNet: temporal 2D-variation modeling for general time series analysis. In: International Conference on Learning Representations (2023)
Rüschendorf, L., Rachev, S.T.: A characterization of random variables with minimum L2-distance. J. Multivar. Anal. 32(1), 48–54 (1990)
Zou, B., Umugwaneza, M.P.: Shape-based trademark retrieval using cosine distance method. In: 2008 Eighth International Conference on Intelligent Systems Design and Applications, vol. 2, pp. 498–504 (2008)
Chen, Y., et al.: ImDiffusion: imputed diffusion models for multivariate time series anomaly detection. In: VLDB (2023)
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Ding, Z., Chen, Y., Wang, H., Wang, X., Zhang, W., Zhang, Y. (2025). TIformer: A Transformer-Based Framework for Time-Series Forecasting with Missing Data. In: Chen, T., Cao, Y., Nguyen, Q.V.H., Nguyen, T.T. (eds) Databases Theory and Applications. ADC 2024. Lecture Notes in Computer Science, vol 15449. Springer, Singapore. https://doi.org/10.1007/978-981-96-1242-0_6
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