Fuzzy time series forecasting models represent a versatile and robust class of predictive techniques that address uncertainty and non-linearity in data. By utilising fuzzy set theory, these models ...
"By open-sourcing our proven Falcon TST model, we aim to advance the field through global collaboration -- inviting scientists worldwide to contribute real-world feedback and accelerate innovation in ...
Pre-trained foundation models are making time-series forecasting more accessible and available, unlocking its benefits for smaller organizations with limited resources. Over the last year, we’ve seen ...
Some of the models used to forecast everything from financial trends to animal populations in an ecosystem are incorrect, ...
This study used SEER data from 1975 to 2018 and included 545,486 patients with lung cancer. The best parameters for ARIMA are ARIMA (p, d, q) = (0, 2, 2). In addition, the best parameter for SES was α ...
Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 80, No. 5 (2018), pp. 975-993 (19 pages) Estimating conditional quantiles of financial time series is essential for ...
In this paper, we propose a simple nonparametric test for testing the null hypothesis of constant coefficients against nonparametric smooth coefficients in a semiparametric varying coefficient model ...
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