Time series forecasting is used in many applications like price prediction, stock trend analysis, etc., This forecasting is dependent on multiple variables, which have an impact on contextual uncertainties. The inability to handle contextual uncertainty, induces incorrect predictions, leading to crucial business decision-making failures. This further restricts the abilities of forecasting and decision-making in a dynamic and partially observable real-life environment. This paper proposes a Turning Points-based approach along with shoulder impact processing. This imparts uncertainties in learning and handles such scenarios more scientifically and gracefully. Turning points are predicted based on probable impending surprise. These turning points are forecasted based on uncertainties found in the variables contributing to sudden out-of-the-pattern changes in the next cycle of forecasting. The shoulder impact areas are classified based on trend changes to improve decisions. Multivariate analysis, fuzzy time series analysis, and contextual impact determination assist to identify such uncertainties and inter- dependencies for improved forecasting. These points support trend forecasting instead of forecasting at an instance. This helps in many applications including decision-making regarding pricing, storing, and distribution of short-span, medium-span, and long-term span perishable commodities. TrIK can be extended to multiple time series forecasting applications to reduce wastage and economic loss.

[1] Adya M., Colloby F., Armstrong J., and Kennedy M., “Automatic Identification of Time Series Features for Rule-Based Forecasting,” International Journal of Forecasting, vol. 17, no. 2, pp. 143-157, 2001. https://doi.org/10.1016/S0169-2070(01)00079-6

[2] Ampountolas A., “Forecasting Hotel Demand Uncertainty Using Time Series Bayesian VAR Models,” Tourism Economics, vol. 25, no. 5, pp. 734-756, 2019. https://doi.org/10.1177/1354816618801741

[3] Bachmann R., Steffen E., and Eric R., “Uncertainty and Economic Activity: Evidence from Business Survey Data,” American Economic Journal: Macroeconomics, vol. 5, no. 2, pp. 217- 249, 2013. Doi: 10.1257/mac.5.2.217

[4] Box G., Jenkins G., Reinsel G., and Ljung G., Time Series Analysis: Forecasting and Control, John Wiley and Sons, 2015. https://www.wiley.com/en- in/Time+Series+Analysis%3A+Forecasting+and +Control%2C+5th+Edition-p-9781118674918

[5] Cao L., “Support Vector Machines Experts for Time Series Forecasting,” Neurocomputing, vol. 51, pp. 321-339, 2003. https://doi.org/10.1016/S0925-2312(02)00577-5

[6] Cress U. and Kimmerle J., “A Systemic and Cognitive View on Collaborative Knowledge Building with Wikis,” International Journal of Computer-Supported Collaborative Learning, vol. 3, pp. 105-122, 2008. https://doi.org/10.1007/s11412-007-9035-z

[7] Dailey R., LeFebvre L., Crook B., and Brody N., “Relational Uncertainty and Communication in On-Again/Off-Again Romantic Relationships: Assessing Changes and Patterns Across Recalled Turning Points,” Western Journal of Communication, vol. 80, no. 3, pp. 239-263, 2016. https://doi.org/10.1080/10570314.2015.1094123

[8] De Oliveira J. and Ludermir T., “A Hybrid Evolutionary Decomposition System for Time Series Forecasting,” Neurocomputing, vol. 180, pp. 27-34, 2016. https://doi.org/10.1016/j.neucom.2015.07.113

[9] Ferreira T., Vasconcelos G., and Adeodato P., “A New Evolutionary Method for Time Series Forecasting,” in Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 2221-2222, 2005. https://doi.org/10.1145/1068009.1068384

[10] Finfgeld-Connett D., “Use of Content Analysis to Conduct Knowledge-Building and Theory- Generating Qualitative Systematic Reviews,” Qualitative Research, vol. 14, no. 3, pp. 341-352. 2014. https://doi.org/10.1177/1468794113481790

[11] Hamzaçebi C., “Improving Artificial Neural Networks’ Performance in Seasonal Time Series Forecasting,” Information Sciences, vol. 178, no. 23, pp. 4550-4559, 2008. https://doi.org/10.1016/j.ins.2008.07.024

[12] Hewitt J. and Marlene S., “Design Principles for Distributed Knowledge Building Processes,” Educational Psychology Review, vol. 10, pp. 75-96, 1998. https://doi.org/10.1023/A:1022810231840

[13] Kasabov N., Evolving Connectionist Systems: The Knowledge Engineering Approach, Springer Science and Business Media, 2007. https://doi.org/10.1007/978-1-84628-347-5

[14] Kasabov N., Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering., Marcel Alencar, 1996. https://books.google.co.in/books/about/Foundati ons_of_Neural_Networks_Fuzzy_Sys.html?id=9 bdwtUQLchIC&redir_esc=y

[15] Kasabov N., Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer Berlin Heidelberg, 2019. https://doi.org/10.1007/978-3-662-57715-8

[16] Khandelwal I., Adhikari R., and Verma G., “Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition,” Procedia Computer Science, vol. 48, pp. 173-179, 2015. https://doi.org/10.1016/j.procs.2015.04.167

[17] Kihoro J., Otieno R., and Wafula C., “Seasonal Time Series Forecasting: A Comparative Study of ARIMA and ANN Models,” Dspace Respository vol. 5, no. 2, 2004. https://repository.cuk.ac.ke/xmlui/handle/123456 789/203

[18] Kim K., “Financial Time Series Forecasting Using Support Vector Machines,” Neurocomputing, vol. 55, no. 1-2, pp. 307-319, 2003. https://doi.org/10.1016/S0925- 2312(03)00372-2

[19] Kimmerle J., Cress U., and Held C., “The Interplay Between Individual and Collective Knowledge: Technologies for Organisational Turning Point Induced Knowledge Forecasting under Uncertainties (TrIK) 231 Learning and Knowledge Building,” Knowledge Management Research and Practice, vol. 8, pp. 33-44, 2010. DOI:10.1057/kmrp.2009.36

[20] Koutsoyiannis D. and Montanari A., “Statistical Analysis of Hydroclimatic Time Series: Uncertainty and Insights,” Water Resources Research, vol. 43, no. 5, 2007. DOI:10.1029/2006WR005592

[21] Kulkarni P., Reinforcement and Systemic Machine Learning for Decision Making, John Wiley and Sons, 2012. https://www.wiley.com/en- us/Reinforcement+and+Systemic+Machine+Lear ning+for+Decision+Making-p-9781118271551

[22] Kulkarni P., Reverse Hypothesis Machine Learning Cham, Switzerland: Springer, 2017. https://doi.org/10.1007/978-3-319-55312-2

[23] Lindley D., Understanding Uncertainty, John Wiley and Sons, 2013. https://doi.org/10.1007/s10182-008-0103-0

[24] Makridakis S. and Bakas N., “Forecasting and Uncertainty: A Survey,” Risk and Decision Analysis, vol. 6, no. 1, pp. 37-64, 2016. DOI:10.3233/RDA-150114

[25] Martínez F., Frías M., Pérez M., and Rivera A., “A Methodology for Applying K-Nearest Neighbor to Time Series Forecasting,” Artificial Intelligence Review, vol. 52, no. 3, pp. 2019-2037, 2019. https://doi.org/10.1007/s10462-017-9593-z

[26] Moerchen F., “Algorithms for Time Series Knowledge Mining,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, pp. 668-673, 2006. https://doi.org/10.1145/1150402.1150485

[27] Mörchen F., Ultsch A., and Hoos O., “Extracting Interpretable Muscle Activation Patterns with Time Series Knowledge Mining,” International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 9, no. 3, pp. 197-208, 2005. DOI:10.3233/KES-2005-9304

[28] Mörchen F. and Ultsch A., “Efficient Mining of Understandable Patterns from Multivariate Interval Time Series,” Data mining and Knowledge Discovery, vol. 15, pp. 181-215, 2007. https://doi.org/10.1007/s10618-007-0070-1

[29] Moskaliuk J., Kimmerle J., and Cress U., “Wiki‐ Supported Learning and Knowledge Building: Effects of Incongruity between Knowledge and Information,” Journal of Computer Assisted Learning, vol. 25, no. 6, pp. 549-561, 2009. https://doi.org/10.1111/j.1365-2729.2009.00331.x

[30] Oshima J., Oshima R., and Matsuzawa Y., “Knowledge Building Discourse Explorer: A Social Network Analysis Application for Knowledge Building Discourse,” Educational Technology Research and Development, vol. 60, pp. 903-921, 2012. https://doi.org/10.1007/s11423-012-9265-2

[31] Panigrahi S. and Behera H., “A Hybrid ETS-ANN Model for Time Series Forecasting,” Engineering Applications of Artificial Intelligence, vol. 66, pp. 49-59, 2017. https://doi.org/10.1016/j.engappai.2017.07.007

[32] Pekkola S. and Ukko J., “Designing a Performance Measurement System for Collaborative Network,” International Journal of Operations and Production Management, vol. 36 no. 11, pp. 1410-1434, 2016. DOI:10.1108/IJOPM-10-2013-0469

[33] Philip D., “The Knowledge Building Paradigm: A Model of Learning for Net Generation Students,” Innovate: Journal of Online Education, vol. 3, no. 5, 2007. https://www.learntechlib.org/p/104343/

[34] Purwanto P. and Eswaran C., “Enhanced Hybrid Prediction Models for Time Series Prediction,” The International Arab Journal of Information Technology, vol. 15, no. 5, pp. 866-874, 2018. https://www.iajit.org/portal/PDF/September%20 2018,%20No.%205/10459.pdf

[35] Singh G., Hawkins L., and Whymark G., “An Integrated Model of Collaborative Knowledge Building,” Interdisciplinary Journal of E- Learning and Learning Objects, vol. 3, no. 1, pp. 85-105, 2007. DOI:10.28945/388

[36] Singh S., “A Simple Method of Forecasting Based on Fuzzy Time Series,” Applied Mathematics and Computation, vol. 186, no. 1, pp. 330-339, 2007. https://doi.org/10.1016/j.amc.2006.07.128

[37] Slater G., St C., and Birney E., “Automated Generation of Heuristics for Biological Sequence Comparison,” BMC Bioinformatics, vol. 6, pp. 1- 11, 2005. https://doi.org/10.1186/1471-2105-6-31

[38] Voyant C., Notton G., Darras C., Fouilloy A., and Motte F., “Uncertainties in Global Radiation Time Series Forecasting Using Machine Learning: The Multilayer Perceptron Case,” Energy, vol. 125, pp. 248-257, 2017. https://doi.org/10.1016/j.energy.2017.02.098

[39] West M., “Bayesian Forecasting of Multivariate Time Series: Scalability, Structure Uncertainty and Decisions,” Annals of the Institute of Statistical Mathematics, vol. 72, pp. 1-31, 2020. https://doi.org/10.1007/s10463-019-00741-3

[40] Yang M., “Lag Length and Mean Break in Stationary VAR Models,” The Econometrics Journal, vol. 5, no. 2, pp. 374-386, 2002. DOI:10.1111/1368-423X.00089