The widespread adoption of e-nose devices based on chemiresistive materials has been hindered by issues related to sensor device complexity and reliability, specifically sensor drift, necessitating frequent recalibration and retraining of pattern recognition models. This study introduces a method for thermocycling a single sensor based on a free-standing network of single-walled carbon nanotubes (SWCNTs) to acquire signal patterns for selective analyte detection. Additionally, it employs a data filtering technique to compensate for the sensor drift. A free-standing SWCNT film, only a few nanometers thick, is thermally cycled via Joule heating between room temperature and 120 °C. Under these conditions, the sensitivity was tested towards NO2, H2S, and acetone vapors (10–25 ppm) in the mixture with dry air. Signal patterns produced through thermocycling were processed using CatBoost and LSTM algorithms. The accuracy of detection reached 90 % in the classification task, and the average root mean squared error of analyte concentration detection in the multioutput regression task was below 4 ppm. By combining original sensor design, thermocycling, signal filtering for drift compensation, and advanced pattern recognition models, this work contributes to overcoming the challenges in multivariate sensing systems, paving the way for practical applications of the more reliable chemiresistive sensors.