Abstract: Climate change is the observed and projected long­term changes in average climate, as well as climate variability, caused by human activity, including such anomalies as droughts, severe storms and floods. The climate of our planet has been constantly changing throughout the entire geological history of the Earth, and these changes were accompanied by significant fluctuations in average global temperatures. One of the main drivers of global average temperature change is the burning of fossil fuels, coal, gas and oil, which has increased the concentration of greenhouse gases such as carbon dioxide in our atmosphere. This work is primarily aimed at studying the problems of predicting such a weather phenomenon as a local change in temperature — based on the time series models presented in this work, it is possible to make the necessary predictions also for measuring the level of snow cover, precipitation level. The use of temperature prediction models (such as Holt­Winter’s method or Prophet library) is of great practical importance, starting from predicting the temperature for the next year for sowing, crop yields, etc. In this work, a long­term analysis of the average daily temperature in the Kyiv region was carried out using the Python programming language and its data analysis libraries Pandas, Numpy, Scikit­Learn, etc. using exponential smoothing models and the Prophet library. These models can be used to process and forecast different types of data, such as: temperature, precipitation, snow depth. In addition, forecasts can be used for various purposes of human activity. The materials of the article have a scientific and methodological nature.