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Cryptocurrency prices have high volatility compared to traditional financial time series. This is the reason why it is hard to predict prices properly using standard models. The aim of this work is to apply the hidden Markov chain model to predict cryptocurrency price direction and to determine regime switching impact to cryptocurrency prices. Various political actions and economic regimes affect cryptocurrency prices. Logarithmic returns used for 2-, 3- ,4- ,5-state hidden Markov model application. It was found that 3-state model is the best to find hidden regimes in cryptocurrency price changes. This model divided cryptocurrency returns according to their volatility into low, medium, and extreme volatility regimes. For the price direction forecast the hidden Markov model was constructed by discretizing the cryptocurrency prices returns into 5 intervals. Based on them, a sequence of observations was formed for 3-year data. The assumption was made that there are two hidden states which represents increasing and decreasing prices. Baum-Welch algorithm was used for learning the model. Then predicted price directions for 3 months data and calculated the accuracy of the model, which was compared with machine learning methods (random forest algorithm, artificial neural network method, and naive Bayesian classifier). In average the prediction made by the hidden Markov model is more accurate than other methods when daily observations are considered.

Cryptocurrency prices have high volatility compared to traditional financial time series. This is the reason why it is hard to predict prices properly using standard models. The aim of this work is to apply the hidden Markov chain model to predict cryptocurrency price direction and to determine regime switching impact to cryptocurrency prices. Various political actions and economic regimes affect cryptocurrency prices. Logarithmic returns used for 2-, 3- ,4- ,5-state hidden Markov model application. It was found that 3-state model is the best to find hidden regimes in cryptocurrency price changes. This model divided cryptocurrency returns according to their volatility into low, medium, and extreme volatility regimes. For the price direction forecast the hidden Markov model was constructed by discretizing the cryptocurrency prices returns into 5 intervals. Based on them, a sequence of observations was formed for 3-year data. The assumption was made that there are two hidden states which represents increasing and decreasing prices. Baum-Welch algorithm was used for learning the model. Then predicted price directions for 3 months data and calculated the accuracy of the model, which was compared with machine learning methods (random forest algorithm, artificial neural network method, and naive Bayesian classifier). In average the prediction made by the hidden Markov model is more accurate than other methods when daily observations are considered.

Cryptocurrency prices have high volatility compared to traditional financial time series. This is the reason why it is hard to predict prices properly using standard models. The aim of this work is to apply the hidden Markov chain model to predict cryptocurrency price direction and to determine regime switching impact to cryptocurrency prices. Various political actions and economic regimes affect cryptocurrency prices. Logarithmic returns used for 2-, 3- ,4- ,5-state hidden Markov model application. It was found that 3-state model is the best to find hidden regimes in cryptocurrency price changes. This model divided cryptocurrency returns according to their volatility into low, medium, and extreme volatility regimes. For the price direction forecast the hidden Markov model was constructed by discretizing the cryptocurrency prices returns into 5 intervals. Based on them, a sequence of observations was formed for 3-year data. The assumption was made that there are two hidden states which represents increasing and decreasing prices. Baum-Welch algorithm was used for learning the model. Then predicted price directions for 3 months data and calculated the accuracy of the model, which was compared with machine learning methods (random forest algorithm, artificial neural network method, and naive Bayesian classifier). In average the prediction made by the hidden Markov model is more accurate than other methods when daily observations are considered.

Cryptocurrency prices have high volatility compared to traditional financial time series. This is the reason why it is hard to predict prices properly using standard models. The aim of this work is to apply the hidden Markov chain model to predict cryptocurrency price direction and to determine regime switching impact to cryptocurrency prices. Various political actions and economic regimes affect cryptocurrency prices. Logarithmic returns used for 2-, 3- ,4- ,5-state hidden Markov model application. It was found that 3-state model is the best to find hidden regimes in cryptocurrency price changes. This model divided cryptocurrency returns according to their volatility into low, medium, and extreme volatility regimes. For the price direction forecast the hidden Markov model was constructed by discretizing the cryptocurrency prices returns into 5 intervals. Based on them, a sequence of observations was formed for 3-year data. The assumption was made that there are two hidden states which represents increasing and decreasing prices. Baum-Welch algorithm was used for learning the model. Then predicted price directions for 3 months data and calculated the accuracy of the model, which was compared with machine learning methods (random forest algorithm, artificial neural network method, and naive Bayesian classifier). In average the prediction made by the hidden Markov model is more accurate than other methods when daily observations are considered.

Il fenomeno dell'abbandono delle campagne e del pro-gressivo inurbamento della popolazione pone alle città al-cune sfide legate alla sostenibilità. Una di queste sfide ri-guarda il cibo, che da un lato deve garantire la nutrizione dei cittadini, dall'altro rappresenta un veicolo culturale per il progresso della società. Così le politiche alimentari ur-bane vanno ben oltre l'esigenza di "nutrire la città", inve-stendo l'innovazione sociale ed economica, le politiche di welfare, il governo delle aree urbane e le rappresentanze democratiche. La centralità delle città in questo contesto mette in discus-sione le politiche euro-unionali impostate a livello nazio-nale, e propone una struttura orizzontale in cui la condivi-sione delle buone pratiche è elemento chiave per riformare in modo sostenibile, equo e sano il sistema agroalimentare mondiale . Al fine di contestualizzare l'attuale stato delle politiche alimentari urbane, la prima parte dell'analisi intende con-centrarsi sull'evoluzione storica e sociale del concetto stesso di food policy nello spazio urbano, delineando gli aspetti principali delle politiche alimentari nello spazio che, come dirò, può essere definito "rurbano". Saranno poi esaminate le scelte politiche tese ad incenti-vare l'applicazione delle nuove tecnologie in ambito agri-colo per migliorare la qualità della produzione sul campo e realizzare l'agricoltura di precisione che molti studiosi considerano la "terza rivoluzione verde": sostenere l'agricoltura intelligente è il primo passo per rendere pos-sibile un sistema produttivo localizzato, a discapito del si-stema di globalizzazione avviato tra il XIX ed il XX seco-lo. Tale ultima tendenza alla localizzazione della produzione alimentare è resa evidente dalla diffusione dei prodotti alimentari locali, e dalla scelta di sostenere sistemi di filie-ra corta per quanto attiene alla produzione alimentare ur-bana, tanto a livello nazionale quanto a livello comunitario ed internazionale. Saranno oggetto dell'elaborato le speci-fiche forme dei sistemi di filiera corta: la vendita diretta, i farmers market, l'e-commerce, i gruppi di acquisto solida-li. A seguire saranno studiate le pratiche indicate general-mente con il termine di "orticoltura urbana": sin dalle loro origine queste riaffermano l'effimera divisione tra città e campagna e tendono a favorire una impostazione di base in favore della localizzazione dei prodotti, proponendosi proprio come esempio di forma di produzione alimentare nello spazio urbano/cittadino. In conclusione, l'elaborato si concentrerà sull'analisi delle forme di città sostenibili alla cui base sono dettate politi-che alimentari in cui la localizzazione della produzione alimentare completa un più ampio stile di vita alternativo al modello delle città industriali globalizzate. In questo senso sarà introdotta la pratica del consumo collaborativo come potenziale mezzo per veicolare positivamente le buone politiche alimentari a livello urbano.

Research of how customers perceive ecological food products has to be conducted in order to define consumer behavior and foresee opportunities of the eco food product growth. Consumer behavior depends on social, economic and cultural context. Thus, research methods have to be developed for every single case, which turns into scientific problem. This article deals with the factors that form consumer perception of the ecological food product, defines basic specific features of this particular market and describes its growth possibilities. Objective – define consumer perception of the ecological food product. Key points: 1. Characterize basic features of the ecological food product important to consumers; 2. Define major characteristics of the ecological food market; 3. Determine possibilities of this particular market growth. Such common research methods as comparative scientific literature analysis and systemic logical analysis have been used when researching and analyzing ecological food products and basic market characteristics. Conclusions: 1. Ecological foods products are perceived by consumers as healthy, safe, nutritious, vitamin rich, tasty, local, expensive, scarce and remarkable food. Some of the features have not been scientifically proven. However, all of these characteristics regardless if seeming or proven influence consumer perception of the ecological food products. 2. Ecological food market could be defined as the market where consumption changes with the time; demand for the ecological precooked food increases and depends on consumer age, sex, education and occupation; ecological food market growth is stimulated by country's government. 3. It is important to reduce ecological food prices, improve its accessibility and unify EU trade marks in order to develop and sustain ecological food market.

Research of how customers perceive ecological food products has to be conducted in order to define consumer behavior and foresee opportunities of the eco food product growth. Consumer behavior depends on social, economic and cultural context. Thus, research methods have to be developed for every single case, which turns into scientific problem. This article deals with the factors that form consumer perception of the ecological food product, defines basic specific features of this particular market and describes its growth possibilities. Objective – define consumer perception of the ecological food product. Key points: 1. Characterize basic features of the ecological food product important to consumers; 2. Define major characteristics of the ecological food market; 3. Determine possibilities of this particular market growth. Such common research methods as comparative scientific literature analysis and systemic logical analysis have been used when researching and analyzing ecological food products and basic market characteristics. Conclusions: 1. Ecological foods products are perceived by consumers as healthy, safe, nutritious, vitamin rich, tasty, local, expensive, scarce and remarkable food. Some of the features have not been scientifically proven. However, all of these characteristics regardless if seeming or proven influence consumer perception of the ecological food products. 2. Ecological food market could be defined as the market where consumption changes with the time; demand for the ecological precooked food increases and depends on consumer age, sex, education and occupation; ecological food market growth is stimulated by country's government. 3. It is important to reduce ecological food prices, improve its accessibility and unify EU trade marks in order to develop and sustain ecological food market.