[Ali, M., Jones, W. M., Xie, X., and Williams, M. (2019), Time cluster: Dimension reduction applied to temporal data for visual analytics. The Visual Computer, 35(6-8), 1013-1026.10.1007/s00371-019-01673-y]Search in Google Scholar
[Cai, J., and Houge, T. (2008). Long-Term Impact of Russell 2000 Index Rebalancing. Financial Analysts Journal, 64(4), 76-91. https://doi.org/10.2469/faj.v64.n4.710.2469/faj.v64.n4.7]Search in Google Scholar
[Gareth, J., Witten, D., Hastie, T., and Tibshirani, R. (2017). An introduction to statistical learning with applications in R. Springer, Springer Texts in Statistics.]Search in Google Scholar
[Hora, S., and Jalbert, T. (2006). The Dow Jones Industrial Average in the twentieth century – Implications for option pricing. Academy of Accounting and Financial Studies Journal, 10(3), 17-40.]Search in Google Scholar
[Hota, H. S., Handa, R., and Shrivas, A. K. (2017). Time series data prediction using sliding window based RBF neural network. International Journal of Computational Intelligence Research, 13(5), 1145-1156.]Search in Google Scholar
[Lerman, D. (2001). Exchange traded funds and e-mini stock index futures. Wiley and Sons.]Search in Google Scholar
[Makridakis, S., Spiliotis, E., and Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3), e0194889. doi: 10.1371/journal. pone.0194889]Search in Google Scholar
[Mozaffari, L., Mozaffari, A., and Azad, N. (2015). Vehicle speed prediction via a sliding-window time series analysis and an evolutionary least learning machine: A case study on San Francisco urban roads. Engineering Science and Technology. An International Journal, 18(2), 150-162. doi: 10.1016/j.jestch.2014.11.00210.1016/j.jestch.2014.11.002]Search in Google Scholar
[Öztürk Katircioğlu, D., Güvenir, H. A., Ravens, U., and Baykal, N. (2017). A window-based time series feature extraction method. Computers in Biology and Medicine, (89), 466-486.10.1016/j.compbiomed.2017.08.011]Search in Google Scholar
[Rajalakshmi, D., and Dinakaran, K. (2015). A survey on effective pattern matching in uncertain time series stream data. Asian Journal of Applied Sciences, (8), 217-226. doi: 10.3923/ajaps.2015.217.22610.3923/ajaps.2015.217.226]Search in Google Scholar
[Senthil, D., and Suseendran, G. (2018). Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine. International Journal of Engineering & Technology, 7(3.3), 218. doi: 10.14419/ijet.v7i2.33.1389010.14419/ijet.v7i2.33.13890]Search in Google Scholar
[Siegel, J. J., and Schwartz J. D. (2006). Long-term returns on the original S&P 500 companies. Financial Analysts Journal, 62(1) 18-31.]Search in Google Scholar
[Spglobal. (2020). Dow Jones Industrial Average®. Retrieved from https://www.spglobal.com/spdji/en/indices/equity/dow-jones-industrial-average/#overview]Search in Google Scholar
[Sverdlov, A. (2015). An overview of machine learning and pattern recognition. Retrieved June 26, 2015 from https://www.gc.cuny.edu/CUNY_GC/media/ComputerScince/Student%20Presentations/Alexander%20Sverdlov/Second_Exam_Survey_Alexander_Sverdlov_6_26_2015.pdf]Search in Google Scholar
[Vafaeipour, M., Rahbari, O., Rosen, M., Fazelpour, F., and Ansarirad, P. (2014). Application of sliding window technique for prediction of wind velocity time series. International Journal of Energy and Environmental Engineering, 5(2-3). doi: 10.1007/s40095-014-0105-510.1007/s40095-014-0105-5]Search in Google Scholar
[Yahmed Y. B., Azuraliza, A. B., RazakHamdan, A., Almahdi, A., and Abdullah, S. M. S. (2015). Adaptive sliding window algorithm for weather data segmentation. Journal of Theoretical and Applied Information Technology, 80(2), 322-333.]Search in Google Scholar
[Yahoo Finance (n.d.). Retrieved from https://finance.yahoo.com/?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAEIeC5nUxiIqbNz7KtFBHz6O9SpJGZNULrSHUh51TuFGXN6I2OZ_v6EZkkSgV_7SoQarvGOESNBrIYN2KWsCeqj1tnTebUyflnSY3MwSqUHEXMOWAs9KzWHDVtnpJLqHcy8x77cLPMJc_MQTq191OAGZ-p7XT_8_FoxraL8NmmmY]Search in Google Scholar
[Yu, Y., Zhu, Y., Li, S., and Wan, D. (2014). Time series outlier detection based on sliding window prediction. Mathematical Problems in Engineering, (4). http://dx.doi.org/10.1155/2014/87973610.1155/2014/879736]Search in Google Scholar
[Zhu, Y. and Shasha, D. (2003). Query by Humming: A time series database approach. (Proc. of ACM Special Interest Group on Management of Data). San Diego, California, USA.]Search in Google Scholar