## 2 Related Work  
Several researchers have employed ARIMA models for forecast of stock prices. As an example, authors in [4; 8] describe as ARIMA can easily handle such a data format and how it is well suited for time series forecasting. One more example is represented by [21] where authors described an ARIMA model with currency as exogenous feature used to forecast commodity prices. ARIMA has also been employed within the agriculture domain where authors in [6] discussed how they have employed it for forecasting crop prices, and results indicate very low error values in terms of MSE (Mean Squared Error) and MAPE (Mean Absolute Percentage Error). Within the same domain, one more work is represented by [7], where authors described how to employ ARIMA for forecast cloud coverage based on ground-based cloud images. In particular, taking into account the correlation about cloud coverage over time, authors obtained an approximate error of 8% of the predictions compared to real values. Energy is one more domain of application for ARIMA. For example, work in [5] describes an ARIMA model for forecasting electricity price during weeks, having as input wavelet transform applied to time series related to electricity prices. The application of wavelet transform further reduces the error of predictions performed by the model. In a similar way, other works related to the forecast of energy have been discussed in [9], where the authors described an ARIMA model for forecast coal's consumption in China, and in [10] where authors used it to forecast energy consumption of Turkey. One more example can be found in [11] where authors describe the use of exogenous variables for residential low voltage distribution networks. It should be observed that, regardless the adopted model, when exogenous variables are not used, there is a limit of predictability [18], whereas the appropriate selection of such variables leads towards an improvement of the forecasting performance [22]. In literature, researchers have already tried to include values returned by Google Trends as exogenous variables in ARIMA models for forecasting of oil consumption [23]. Also, authors in [24] described an ARIMA model using Google Trends modified with Twitter's sentiment score to estimate consumption and sales for a particular business. In fact, Sentiment Analysis is one more domain where ARIMA models have been employed. Moreover, authors in [25] have used sentiment analysis of products users reviews in a time interval to build an ARIMA model to predict the amount of sales of the products. Moreover, authors in [26] focused on forecasting the price of house property and electronic products using sentiment news from Baidu, a well-known portal website in China analysing news content and extracting key events related to the products. Results indicate that predicted prices of products can be influenced by sentiment factors. Similarly, authors in [27], based on the user comments data from a popular professional social networking site of China stock market called Xueqiu, calculated the investor sentiment of each stock and performed their analysis based on the Thermal Optimal Path method. The results show that the sentiment data was not always leading over stock market price but it might be used to predict the stock price only when the stock had high investor attention. Several works have tried to address the concept of stationarity, and seasonality of the input data of an ARIMA model. More in general, authors in [28] described how the problem of time series forecasting should be approached, focusing on the importance, for the forecasting, to remove trends or seasonality of the considered time series. To check whether a given time series is _stationary_ or not, authors in [29] introduced the _Adfuller Test_. They also provided information related to the meaning of stationary series, when they can be considered good for statistics analysis and prediction or not.