A Higher order Markov model for time series forecasting
The values of some time series in the real world usually change randomly but they may contain information from history. In these cases, today value can depend not only on yesterday value but also on further values in history. Hence, a forecast model which takes the information from two or three days ago to predict today value can give a more accurate prediction. This paper presents a novel higher Markov model for time series forecasting
where the state space of the Markov chain was contructed from different levels of changes of the time series. Once the transition matrix has been calculated based on the fuzzy sets, the mean of the levels of changes along with transition probabilities allow caculating the data for forecast values. The experiment with different data shows a significantly improved accuracy compared to other previous models such as ARIMA, ANN , HMM-based models and combined HMM-Fuzzy models.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.