Machine learning model in time series

Abstract

Machine learning is a significant part of human technology as it offers a wide range of applications daily, from internet search to talking robots. Time series forecasting is one of many applications that are provided and it has important role in many industries.Machine learningtime seriesforecastingis the main topic of thesis and it will be analyzed a specific approach. Currently, weather prediction as long as air pollution predictions are used extensively with great success. The big advantage of time sequence predictions is that many issues in the near future can be preventedby giving a short-termperiod for preparation before the issue occurs. Based on that argumentcan onesay that Particular Mater air pollution prediction is a very important achievement. The question we answer is how a machine learning model can respond to that needs and how accurate can it be. To achieve this statement, we worked with time series analysis practices as long as with machine learning libraries which are specialized for this type of predictions. The methodology is based on best practices according to documentation and the results of the model are analyzed in detail.