Kotan, KurbanKotan, BayramKirisoglu, Serdar2025-10-112025-10-1120252169-3536https://doi.org/10.1109/ACCESS.2025.3567743https://hdl.handle.net/20.500.12684/21828This study investigates the use of natural language processing language representation models as an early warning system for economic crises, and compares the performance of time series analysis and machine learning models in financial markets before and during the economic crises in order to select the best model. The data used in the research was collected based on the economic crises that occurred in Turkey in December 2021. The aim is to identify an economic crises period by using language representation models for economic news between August 2021 and January 2022. After identifying the economic crises period, short term (1 day), medium term (15 days) and long term (30 days) forecasts were made for the index of thirty companies with the highest trading volume (BIST30) of Borsa Istanbul between 01/01/2021 and 31/12/2021 and performance comparisons were made between the models. The aim is to develop an effective smart, automatic crises detection and forecasting model selection application. The CHIT algorithm introduced in the study is a new missing data filling algorithm used in time series forecasting comparisons. Since the CHIT algorithm has a high impact on the model performance, this algorithm is used in the pre-processing step and comparisons are made.en10.1109/ACCESS.2025.3567743info:eu-repo/semantics/openAccessArtificial intelligencetime seriesmachine learningimputationnatural language processingArtificial intelligencetime seriesmachine learningimputationnatural language processingArticle1383254832732-s2.0-105004699542WOS:001489666400025Q1Q2