ABDUL HAKIM, S. E., 2018. Analisis Kemiskinan di Jawa Tengah.
Bappeda Jateng. Sekilas SDGs [online] Tersedia di: https://sdgs.bappenas.go.id/sekilas-sdgs/#:~:text=TPB%2FSDGs%20merupakan%20komitmen%20global,Bersih%20dan%20Terjangkau%3B%20(8) [Diakses 10 Juli 2023].
BPS Jateng, 2022. Kemiskinan [online] Tersedia di : https://jateng.bps.go.id/indicator/23/34/7/kemiskinan.html [Diakses 23 Maret 2023].
CARVALHO, T., VELLASCO, M., & AMARAL, J. F., 2023. Automatic generation of fuzzy inference systems for multivariate time series forecasting. Fuzzy Sets and Systems, 470, 108657. doi: 10.1016/j.fss.2023.108657.
CHANG, P. C., WANG, Y. W., & LIN, C. H., 2007. The development of a weighted evolving fuzzy neural network for PCB sales forecasting. Expert Systems with Applications, 32(1), 86-96. doi: 10.1016/j.eswa.2005.11.021.
CHEN, S. M., & CHEN, S. W., 2014. Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships. IEEE Transactions on Cybernetics, 45(3), 391-403. doi: 10.1109/TCYB.2014.2326888.
CHEN, S. M., & PHUONG, B. D. H., 2017. Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors. Knowledge-Based Systems, 118, 204-216. doi: 10.1016/j.knosys.2016.11.019.
CHENG, S. H., CHEN, S. M., & JIAN, W. S., 2015, October. A novel fuzzy time series forecasting method based on fuzzy logical relationships and similarity measures. In 2015 IEEE International Conference on Systems, Man, and Cybernetics (pp. 2250-2254). IEEE. doi: 10.1109/SMC.2015.393.
CHENG, S. H., CHEN, S. M., & JIAN, W. S., 2016. Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures. Information Sciences, 327, 272-287. doi: 10.1016/j.ins.2015.08.024.
CHRISTYAWAN, T. Y., SYAUQI HARIS, M., RODY, R., & MAHMUDY, W., 2018. Optimization of Fuzzy Time Series Interval Length Using Modified Genetic Algorithm for Forecasting. International Conference on Sustainable Information Engineering and Technology (SIET), pp. 60-65, doi: 10.1109/SIET.2018.8693219.
COSTA, M. A., RUIZ-CÁRDENAS, R., MINETI, L. B., & PRATES, M. O., 2021. Dynamic time scan forecasting for multi-step wind speed prediction. Renewable Energy, 177, 584-595. doi: 10.1016/j.renene.2021.05.160.
del CAMPO, R. G., GARMENDIA, L., RECASENS, J., & MONTERO, J., 2017, July. Hesitant fuzzy sets and relations using lists. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-6). IEEE. doi: 10.1109/FUZZ-IEEE.2017.8015516.
FIRMANSYAH, A., HASIBUANG, H. F., & KHAIRUNNISA, D., 2023. Addressing the Ideal Implementation of Regional Expenditure to Alleviate Poverty: A Case Study of Kebumen Regency. IPSAR (International Public Sector Accounting Review), 1(1). doi: 10.31092/ipsar.v1i1.2130.
GAJAMANNAGE, K., PARK, Y., & JAYATHILAKE, D. I., 2023. Real-time forecasting of time series in financial markets using sequentially trained dual-LSTMs. Expert Systems with Applications, 223, 119879. doi: 10.1016/j.eswa.2023.119879.
GARG, B., BEG, M. S., & ANSARI, A. Q., 2012, August. A new computational fuzzy time series model to forecast number of outpatient visits. In 2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS) (pp. 1-6). IEEE. doi: 10.1109/NAFIPS.2012.6290977.
HARMADJI, D. E., SOLIKHIN, S., YUDATAMA, U., & PURWANTO, A., 2023. Prediksi Produksi Biofarmaka Menggunakan Model Fuzzy Time Series dengan Pendekatan Percentage Change dan Frequency Based Partition. Jurnal Teknologi Informasi dan Ilmu Komputer, 10(1), 173-184. doi: 10.25126/jtiik.20231016267.
HARTOMO, K. D., YULIANTO, S., & VALENTINA, A., 2020. A New Model of Poverty Index Prediction Using Triple Exponential Smoothing Method. In 2020 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) (pp. 76-79). IEEE. doi: 10.1109/ICITACEE50144.2020.9239205.
HOLT, C. C., 2004. Forecasting seasonals and trends by exponentially weighted moving averages. International journal of forecasting, 20(1), 5-10. doi: 10.1016/j.ijforecast.2003.09.015.
JAISWAL, R., JHA, G. K., KUMAR, R. R., & CHOUDHARY, K., 2022. Deep long short-term memory based model for agricultural price forecasting. Neural Computing and Applications, 34(6), 4661-4676. doi.org/10.1007/s00521-021-06621-3.
JANA, P., 2016. Aplikasi triple exponential smoothing untuk forecasting jumlah penduduk miskin. Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika, 3(2), 76-82. doi: 10.31316/j.derivat.v3i2.719.
JIANG, J., WU, L., ZHAO, H., ZHU, H., & ZHANG, W., 2023. Forecasting movements of stock time series based on hidden state guided deep learning approach. Information Processing & Management, 60(3). doi: 103328. 10.1016/j.ipm.2023.103328.
JIANG, J. A., SYUE, C. H., WANG, C. H., LIAO, M. S., SHIEH, J. S., & WANG, J. C., 2022. Precisely forecasting population dynamics of agricultural pests based on an interval type-2 fuzzy logic system: Case study for oriental fruit flies and the tobacco cutworms. Precision Agriculture, 23(4), 1302-1332. doi: 10.1007/s11119-022-09886-3.
JIANG, P., YANG, H., LI, R., & LI, C., 2020. Inbound tourism demand forecasting framework based on fuzzy time series and advanced optimization algorithm. Applied Soft Computing, 92, 106320. doi: 10.1016/j.asoc.2020.106320.
JILANI, T. A., BURNEY, S. M. A., & ARDIL, C., 2007. Fuzzy metric approach for fuzzy time series forecasting based on frequency density based partitioning. International Journal of Computational Intelligence, 4(1), 112-117. doi: 10.5281/zenodo.1077541.
KUSHWAH, A. K., & WADHVANI, R., 2022. Trend triplet-based data clustering for eliminating nonlinear trend components of wind time series to improve the performance of statistical forecasting models. Multimedia Tools and Applications, 81(23), 33927-33953. doi: 10.1007/s11042-022-12992-z.
LEWIS, C. D., 1982. Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. (No Title).
MAKATJANE, K., & MOROKE, N., 2016. Comparative study of holt-winters triple exponential smoothing and seasonal Arima: forecasting short term seasonal car sales in South Africa. Makatjane KD, Moroke ND. doi: 10.22495/rgcv6i1art8.
MAKRIDAKIS, S., WHEELWRIGHT, S. C., & HYNDMAN, R. J., 2008. Forecasting methods and applications. John wiley & sons. doi: hdl.handle.net/11728/6581.
MIRCETIC, D., ROSTAMI-TABAR, B., NIKOLICIC, S., & MASLARIC, M., 2022. Forecasting hierarchical time series in supply chains: an empirical investigation. International Journal of Production Research, 60(8), 2514-2533. doi: 10.1080/00207543.2021.1896817.
RUBIO, A., BERMỦDEZ, J. D., & VERCHER, E., 2017. Improving stock index forecasts by using a new weighted fuzzy-trend time series method. Expert Systems with Applications, 76, 12-20. doi: 10.1016/j.eswa.2017.01.049.
SARI, D. A., 2016. Analisis faktor-faktor yang mempengaruhi kesejahteraan masyarakat di Kota Bandarlampung.
SINGH, P., 2017. An efficient method for forecasting using fuzzy time series. In Emerging research on applied fuzzy sets and intuitionistic fuzzy matrices (pp. 287-304). IGI Global. doi: 10.4018/978-1-5225-0914-1.
SOFO, F., & WICKS, A., 2017. An occupational perspective of poverty and poverty reduction. Journal of Occupational Science, 24(2), 244-249. doi: 10.1080/14427591.2017.1314223.
SOLIKHIN, S., & YUDATAMA, U., 2019. Fuzzy Time Series dan Algoritme Average Based Length untuk Prediksi Pekerja Migran Indonesia. Jurnal Teknologi Informasi dan Ilmu Komputer, 6(4), 369-376. doi: 10.25126/jtiik.2019641177.
SOLIKHIN, S., LUTFI, S., PURNOMO, P., & HARDIWINOTO, H., 2021. Prediction of passenger train using fuzzy time series and percentage change methods. Bulletin of Electrical Engineering and Informatics, 10(6), 3007-3018. doi:10.11591/eei.v10i6.2822.
SOLIKHIN, S., LUTFI, S., PURNOMO, P., & HARDIWINOTO, H., 2022. A machine learning approach in Python is used to forecast the number of train passengers using a fuzzy time series model. Bulletin of Electrical Engineering and Informatics, 11(5), 2746-2755. doi: 10.11591/eei.v11i5.3518.
SONG, Q., & CHISSOM, B. S., 1993. Forecasting enrollments with fuzzy time series—Part I. Fuzzy sets and systems, 54(1), 1-9. doi: 10.1016/0165-0114(93)90355-L.
SONG, Q., & CHISSOM, B. S., 1994. Forecasting enrollments with fuzzy time series—Part II. Fuzzy sets and systems, 62(1), 1-8. doi: 10.1016/0165-0114(94)90067-1.
STEVENSON, M., & PORTER, J. E., 1972. Fuzzy time series forecasting using percentage change as the universe of discourse. Change, 1971(3.89), 464-467. doi: 10.5281/zenodo.1069993.
STURGES, H. A., 1926. The choice of a class interval. Journal of the american statistical association, 21(153), 65-66. doi: 10.1080/01621459.1926.10502161.
SUDARSHAN, V. K., BRABRAND, M., RANGE, T. M., & WIIL, U. K., 2021. Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study. Computers in Biology and Medicine, 135, 104541. doi: 10.1016/j.compbiomed.2021.104541.
TATINATI, S., WANG, Y., & KHONG, A. W., 2020. Hybrid method based on random convolution nodes for short-term wind speed forecasting. IEEE Transactions on Industrial Informatics, 18(10), 7019-7029. doi: 10.1109/TII.2020.3043451.
WANG, B., LIU, X., CHI, M., & LI, Y., 2023. Bayesian network based probabilistic weighted high-order fuzzy time series forecasting. Expert Systems with Applications, 121430. doi: 10.1016/j.eswa.2023.121430.
ZADEH, L. A., KLIR, G. J., & YUAN, B., 1996. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers (Vol. 6). World scientific. doi: 10.1016/S0019-9958(65)90241-X.
ZHAO, E., DU, P., & SUN, S., 2022. Historical pattern recognition with trajectory similarity for daily tourist arrivals forecasting. Expert Systems with Applications, 203, 117427. doi: 10.1016/j.eswa.2022.117427.
ZHU, C., MA, X., ZHANG, C., DING, W., & ZHAN, J., 2023. Information granules-based long-term forecasting of time series via BPNN under three-way decision framework. Information Sciences, 634, 696-715. doi: 10.1016/j.ins.2023.03.133.