1. Rasheed, F., Alshalalfa, M., & Alhajj, R. (2010). Efficient
periodicity mining in time series databases using suffix trees.
IEEE Transactions on Knowledge and Data Engineering,
23(1), 79-94.
2. Almeida, A., Brás, S., Sargento, S., & Pinto, F. C. (2023).
Time series big data: a survey on data stream frameworks,
analysis and algorithms. Journal of Big Data, 10(1), 83.
3. Shu, W., Wang, W., & Wang, Y. (2014). A novel energy-
efficient resource allocation algorithm based on immune clonal
optimization for green cloud computing. EURASIP Journal
on Wireless Communications and Networking, 2014(1), 1-9.
4. Yu, D., & Xiao, J. (2022). Multilevel Information Granule
Construction Model Based on Large Data Environment and
Its Application in Time Series. Mobile Information Systems,
2022.
5. Michael H. Zhu and Suyog Gupta. pruning for model
compression - arxiv.org.
6. Yin, X. X., Miao, Y., & Zhang, Y. (2022). Time series-based
data explorer and stream analysis for anomaly prediction.
Wireless Communications and Mobile Computing, 2022.
7. Bhattarai, B. P., Paudyal, S., Luo, Y., Mohanpurkar, M.,
Cheung, K., Tonkoski, R., ... & Zhang, X. (2019). Big
data analytics in smart grids: state‐of‐the‐art, challenges,
opportunities, and future directions. IET Smart Grid, 2(2),
141-154.
8. Ryabko, D., & Mary, J. (2012). Reducing statistical time-
series problems to binary classification. Advances in Neural
Information Processing Systems, 25.
9. Barez, F., Bilokon, P., & Xiong, R. (2023). Benchmarking
Specialized Databases for High-frequency Data. arXiv
preprint arXiv:2301.12561.
10. Pietron, M., & Wielgosz, M. (2020). Retrain or not retrain?-
Efficient pruning methods of deep CNN networks. In
Computational Science–ICCS 2020: 20th International
Conference, Amsterdam, The Netherlands, June 3–5, 2020,
Proceedings, Part III 20 (pp. 452-463). Springer International
Publishing.
11. Dhananjoy Dey Vidhi Agrawal, Gajraj Kuldeep. Near lossless
time series data compression methods using statistics.
12. Chirikhin, K. S., & Ryabko, B. Y. (2019). Application of data
compression techniques to time series forecasting. arXiv
preprint arXiv:1904.03825.
13. Marjai, P., Lehotay-Kéry, P., & Kiss, A. (2021). The use of
template miners and encryption in log message compression.
Computers, 10(7), 83.
14. Zhou, Y., Ren, H., Li, Z., Wu, N., & Al-Ahmari, A. M. (2021).
Anomaly detection via a combination model in time series
data. Applied Intelligence, 51, 4874-4887.
15. Gómez-Brandón, A., Paramá, J. R., Villalobos, K.,
Illarramendi, A., & Brisaboa, N. R. (2021). Lossless
compression of industrial time series with direct access.
Computers in Industry, 132, 103503.