The purpose of this thesis was to investigate whether some successful ideas in NLP, such as word2vec, are applicable to time series prob- lems or not. More specifically, we are interested to assess a combina- tion of previously proven methods such as SAX and Word2vec. Based on a rolling window approach, we applied SAX to create words for each window. These words formed a corpus on which we performed Word2vec, which served as inputs in a time series forecasting setting. We found that for forecasting horizons of longer length, our proposed method showed an improvement over statistical models under certain conditions. The findings suggest that bringing tools from the natural language processing domain into the time series domain may be an ef- fective idea. Further research is necessary to broaden the knowledge of these types of methods by testing alternative options for the cre- ation of words. Hopefully, this work will motivate other researchers to investigate this type of solution further.