[1] Armaan, M. S. A., Antony A. S., 2019, A comparison of regression models for prediction of
graduate admissions, 2019 International Conference on Computational Intelligence in Data
Science (ICCIDS), Chennai, India.
[2] Armstrong, J. S. (Ed.). (2001), Principles of Forecasting: A Handbook for Researchers and
Practitioners (Vol. 30). Boston, MA: Kluwer Academic.
[3] Banerjee, D., 2014, Forecasting of Indian stock market using time-series ARIMA model. In
2014 2nd international conference on business and information management (ICBIM) (pp.
131-135). IEEE.
[4] Bharathi, S., & Geetha, A. (2017), ”Sentiment analysis for effective stock market prediction.”
In International Journal of Intelligent Engineering and Systems, 10(3), 146-154.
[5] Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992), ”A training algorithm for optimal
margin classifiers.” In Proceedings of the fifth annual workshop on computational learning
theory, 144-152. New York, NY, USA: ACM.
[6] Box, G., 2013, Box and Jenkins: time series analysis, forecasting and control. In A Very
British Affair: Six Britons and the Development of Time Series Analysis During the 20th
Century, London, UK, Palgrave Macmillan UK.
[7] Breiman, L., 2001, Random forests. Machine Learning 45, 5-32.
https://doi.org/10.1023/A:1010933404324[8] Chandwani, D., & Saluja, M. S. (2014), ”Stock direction forecasting techniques: An empirical
study combining machine learning system with market indicators in the Indian context.” In
International Journal of Computer Applications, 92(11), 8-17.
[9] Chatfield, C., 2000, Time-series forecasting, CRC Press, 263pp.
[10] Chatzis, S. P., Siakoulis, V., Petropoulos, A., Stavroulakis, E., Vlachogiannakis, N. ,2018,
Forecasting stock market crisis events using deep and statistical machine learning techniques.
Expert Systems with Applications, 112, 353-371. https://doi.org/10.1016/j.eswa.2018.06.032
[11] Dharmawan, P. A. S., & Indradewi, I. G. A. A. D. (2021), ”Double exponential smoothing
brown method towards sales forecasting system with a linear and non-stationary data trend.”
In Journal of Physics: Conference Series, IOP Publishing.
[12] Dhyani, B., Kumar, M., Verma, P., Jain, A. ,2020, Stock market forecasting technique using
ARIMA model, International Journal of Recent Technology and Engineering, 8(6), 2694-2697.
https://doi.org/10.35940/ijrte.f8405.038620
[13] Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. J., & Vapnik, V. (1997), ”Support
vector regression machines.” In Advances in Neural Information Processing Systems, 9, 155-
161.
[14] Du, Y., 2018, Application and analysis of forecasting stock price index based on combination
of ARIMA model and BP neural network. In 2018 Chinese control and decision conference
(CCDC) 2854-2857. IEEE.
[15] Fildes, R., & Kourentzes, N. (2011), ”Validation and forecasting accuracy in models of climate
change.” In International Journal of Forecasting, 27(4), 968-995.
[16] Ghanbari, M., Arian, H. ,2019, Forecasting stock market with support vector
regression and butterfly optimization algorithm, arXiv preprint arXiv:1905.11462.
https://doi.org/10.48550/arXiv.1905.11462
[17] H.-I. Lim, 2019, A Linear Regression Approach to Modeling Software Characteristics for
Classifying Similar Software, in 2019 IEEE 43rd Annual Computer Software and Applications
Conference (COMPSAC).
[18] Hansun, S., & Subanar, S. (2016). ”H-WEMA: A New Approach of Double Exponential
Smoothing Method.” In TELKOMNIKA (Telecommunication Computing Electronics and
Control), 14(2), 772-777. http://doi.org/10.12928/telkomnika.v14i2.3096
[19] Hidayatulah, H., & Parasian, S. (2020), ”Comparison of forecasting accuracy rate of exponential smoothing method on admission of new students.” In Journal of Critical Review, 7(2),
268-274.
[20] Hyndman, R. J., & Athanasopoulos, G. (2018), Forecasting: Principles and Practice. OTexts.
292 pp.
[21] Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002), ”A state space framework
for automatic forecasting using exponential smoothing methods.” In International Journal
of Forecasting, 18(3), 439-454. https://doi.org/10.1016/S0169-2070(01)00110-8
[22] Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., Alfakeeh, A. S. ,2020,
Stock market prediction using machine learning classifiers and social media, news. Journal of
Ambient Intelligence and Humanized Computing, 13, 1-24. https://doi.org/10.1007/s12652-
020-01839-w
[23] Khemavuk, P., & Leenatham, A. (2020), ”A Conceptual Model for Uncertainty Demand Forecasting by Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Based on
Quantitative and Qualitative Data.” In International Journal of Operations and Quantitative
Management, 26(4), 285-302. http://dx.doi.org/10.46970/2021.26.4.3
[24] Kucharavy, D., Damand, D., & Barth, M. (2023). ”Technological forecasting using mixed
methods approach.” In International Journal of Production Research, 61(16), 5411-5435.
https://doi.org/10.1016/j.trc.2018.03.001
[25] Kumar, M., Thenmozhi, M. (2014). ”Forecasting stock index returns using ARIMA-SVM,
ARIMA-ANN, and ARIMA-random forest hybrid models.” In International Journal of Banking, Accounting and Finance, 5(3), 284-308. https://doi.org/10.1504/IJBAAF.2014.064307
[26] Makridakis, S., Spiliotis, E., Assimakopoulos, V. (2018). ”Statistical and Machine Learning
forecasting methods: Concerns and ways forward.” In PloS one, 13(3), e0194889. https:
//doi.org/10.1371/journal.pone.0194889
[27] Mallikarjuna, M., Rao, R. P. (2019). ”Evaluation of forecasting methods from selected
stock market returns.” In Financial Innovation, 5(1), 1-16. https://doi.org/10.1186/
s40854-019-0157-x
[28] Meher, B. K., Hawaldar, I. T., Spulbar, C. M., Birau, F. R. (2021). ”Forecasting stock market
prices using mixed ARIMA model: A case study of Indian pharmaceutical companies.” In
Investment Management and Financial Innovations, 18(1), 42-54. http://dx.doi.org/10.
21511/imfi.18(1).2021.04
[29] Meneghini, M., Anzanello, M., Kahmann, A., & Tortorella, G. (2018). ”Quantitative demand
forecasting adjustment based on qualitative factors: case study at a fast food restaurant.” In
Sistemas & Gest˜ao, 13(1), 68-80.
[30] Mondal, P., Shit, L., Goswami, S. (2014). ”Study of effectiveness of time series Modelling
(ARIMA) in forecasting stock prices.” In International Journal of Computer Science, Engineering and Applications, 4(2), 1329. https://doi.org/10.5121/ijcsea.2014.4202
[31] Naik, N., & Mohan, B. R. (2021). ”Novel stock crisis prediction techniquea study on Indian
stock market.” In IEEE Access, 9, 86230-86242. https://doi.org/10.1109/ACCESS.2021.
3088999
[32] Peng, Z., Li X. (2018). ”Application of a multi-factor linear regression model for stock portfolio optimization.” In 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS).
[33] Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Taieb, S. B.,
Ziel, F. (2022). ”Forecasting: theory and practice.” In International Journal of Forecasting,
38(3), 705-871. https://doi.org/10.1016/j.ijforecast.2021.11.001
[34] Roopa, H., Asha, T. (2019). ”A linear model based on principal component analysis for
disease prediction.” In IEEE Access, 7, pp. 105314-105318, 2019.
[35] Scornet, E., Biau G., Vert, J.-P. (2015). ”Consistency of random forests.” In Annals of
Statistics.
[36] Scott, A. J., Fred, C. (2001). Principles of forecasting: a handbook for researchers and
practitioners. Boston, MA: Kluwer Academic, 862 pp.
[37] Shah, D., Isah, H., & Zulkernine, F. (2019), ”Stock market analysis: A review and taxonomy
of prediction techniques.” In International Journal of Financial Studies, 7(2), 26.
[38] Shukor, S. A., Sufahani, S. F., Khalid, K., Abd Wahab, M. H., Idrus, S. Z. S., Ahmad,
A., & Subramaniam, T. S. (2021, May). ”Forecasting Stock Market Price of Gold, Silver,
Crude Oil, and Platinum by Using Double Exponential Smoothing, Holts Linear Trend, and
Random Walk.” In Journal of Physics: Conference Series (Vol. 1874, No. 1, p. 012087).
IOP Publishing. https://doi.org/10.1088/1742-6596/1874/1/012087
[39] Siami-Namini, S., Tavakoli, N., Namin, A. S. (2018). ”A comparison of ARIMA and LSTM
in forecasting time series.” In 17th IEEE International Conference on Machine Learning and
Applications (ICMLA), pp. 1394-1401. https://doi.org/10.1109/ICMLA.2018.00229
[40] Sidqi, F., & Sumitra, I. D. (2019). ”Forecasting product selling using single exponential
smoothing and double exponential smoothing methods.” In IOP conference series: materials
science and engineering.
[41] Sonkavde, G., Dharrao, D. S., Bongale, A. M., Deokate, S. T., Doreswamy, D., & Bhat, S. K.
(2023). ”Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models:
A Systematic Review, Performance Analysis and Discussion of Implications.” In International
Journal of Financial Studies, 11(3), 94. https://doi.org/10.3390/ijfs11030094
[42] Taylor, J. W. (2008). ”An evaluation of methods for very short-term time series forecasting.” In International Journal of Forecasting, 24(4), 635-642. https://doi.org/10.1016/j.
ijforecast.2008.07.007
[43] Uyank, G. K., Guler, N. (2013). ”A study on multiple linear regression analysis.” In ¨ ProcediaSocial and Behavioral Sciences, 106, 234-240. https://doi.org/10.1016/j.sbspro.2013.12.
027
[44] Voyant, C., Notton, G., Kalogirou, S., Nivet, M. L., Paoli, C., Motte, F., Fouilloy, A. (2017).
”Machine learning methods for solar radiation forecasting: A review.” In Renewable energy,
105, 569-582. https://doi.org/10.1016/j.renene.2016.12.095
[45] Wager, S., & Athey, S. (2018). ”Estimation and inference of heterogeneous treatment effects
using random forests.” In Journal of the American Statistical Association, 113, 1228-1242.
https://doi.org/10.48550/arXiv.1510.04342
[46] Wang, X., Sun X. (2016). ”An improved weighted naive Bayesian classification algorithm
based on multivariable linear regression model.” In 2016 9th International Symposium on
Computational Intelligence and Design (ISCID).
[47] Wu, J., Liu, C., Cui W., & Zhang Y. (2019). ”Personalized Collaborative Filtering Recommendation Algorithm based on Linear Regression.” In 2019 IEEE International Conference
on Power Data Science (ICPDS).
[48] Wu, Y., Tan, H., Qin, L., Ran, B., & Jiang, Z. (2018). ”A hybrid deep learning based traffic
flow prediction method and its understanding.” In Transportation Research Part C: Emerging
Technologies, 90, 166-180.
[49] Zeroual, A., Harrou, F., Dairi, A., & Sun, Y. (2020). ”Deep learning methods for forecasting COVID-19 time-series data: A Comparative study.” In Chaos, solitons & fractals, 140,
110121. https://doi.org/10.1016/j.chaos.2020.110121
[50] Zhang, F., & O’Donnell, L. J. (2020). ”Support vector regression.” In Machine learning,
123-140, Academic Press. https://doi.org/10.1016/B978-0-12-815739-8.00007-9