The forecasting model of Bitcoin price with fuzzy time series Markov chain and chen logical method

Ramadani, Kiki; Devianto, Dodi

doi:10.1063/5.0032178

Cited by 12 publications

(12 citation statements)

References 13 publications

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“…Fuzzy logic is one of the most effective methods for handling uncertainties in dynamic and nonstationary environments [16]. Table 1 lists various hybrid fuzzy models that have been used for predicting time-series [17][18][19][20][21][22]. Fuzzy systems, especially hybrid fuzzy models, have been very promising for solving complex problems, where a model estimates and predicts the similarity between two time-series in uncertain conditions [18,[23][24][25][26][27].…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Type-2 Fuzzy Time Warping (DT2FTW): A Hybrid Model for Uncertain Time-Series Prediction

Safari¹,

Hosseini²,

Mazinani³

2021

IJFIS

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Prediction of time series is associated with nondeterministic pattern analysis for uncertain conditions. Therefore, it is necessary to develop high-quality prediction methods for real-world applications. Type-2 fuzzy systems can handle high-order uncertainties, such as sequential dependencies associated with time series. Precise and reliable prediction can help to develop reasonable strategies and assist specialists in planning the best policies for modeling events in uncertain time series. In this study, a hybrid model (dynamic type-2 fuzzy time warping [DT2FTW]) was proposed for handling high-order uncertainties in time-series prediction. A type-2 fuzzy intelligent system was developed alongside a dynamic time warping algorithm for predicting the patterns' similarity in long-time series for time-series prediction. The results demonstrate that the proposed DT2FTW model yields more reliable predictions on global standard benchmarks such as the Mackey-Glass, Dow Jones, and NASDAQ time-series. The results also confirm that the proposed DT2FTW model has lower error rates than its counterpart algorithms in terms of the root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MPE). In addition, the results confirm the superiority of the proposed model with an average area under the ROC curve (AUC) of 94%, with the 95% confidence interval (92%-95%).

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic Type-2 Fuzzy Time Warping (DT2FTW): A Hybrid Model for Uncertain Time-Series Prediction

Safari¹,

Hosseini²,

Mazinani³

2021

IJFIS

View full text Add to dashboard Cite

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“…And the used vector autoregression to identify two positive feedback loops. In 2020, Ramadani and Devianto [5] developed the forecasting model of Bitcoin price with fuzzy time series Markov chain and Chen logical method. Fuzzy time series can model various types of time series data pattern because this method is free from classical assumption.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Cryptocurrency Market by Using q‐Rung Orthopair Fuzzy Hypersoft Set Algorithm Based on Aggregation Operators

et al. 2022

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One of the most important innovations brought by digitization is the cryptocurrency, also called virtual or digital currency, which has been discussed in recent years and in particular is a new platform for investors. Different types of cryptocurrencies such as Bitcoin, Ethereum, Binance Coin, and Tether do not depend on a central authority. Decision making is complicated by categorization and transmission of uncertainty, as well as verification of digital currency. The weighted average and weighted geometric aggregation operators are used in this article to define a multi-attribute decision-making approach. This work investigates the uniqueness of q-rung orthopair fuzzy hypersoft sets (q-ROFHSS), which respond to instabilities, uncertainty, ambiguity, and imprecise information. This research also covers some fundamental topics of q-ROFHSS. The model offered here is the best option for learning about electronic currency. This study validates the complexity of decision-making problems with different attributes and subattributes to obtain an optimal choice. We conclude that Bitcoin has a diverse set of applications and that crypto assets are well positioned to become an important asset class in decision making.

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“…The classical time series model of autoregressive integrated moving average (ARIMA) is often used to produce accurate short memory modeling [1]. In nonlinear time series data, the accuracy of the ARIMA model in forecasting is quite good than the recurrence quantification analysis (RQA) predictive model [2]. However, the heteroscedasticity in the ARIMA model can be corrected by using a variance model, that is the GARCH model [3] or the GARCH exponential [4] or the mixed memory MMGARCH [5].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the forecasting method of FTSMC was also carried out on gold prices as the investment information [14]. In addition, three FTS methods, namely FTS Chen, FTS Segmented Chen, and FTSMC are compared to forecast bitcoin prices [2]. Based on the mean absolute percentage error (MAPE) accuracy value, the FTSMC method still gave better results.…”

Section: Introductionmentioning

confidence: 99%

The hybrid model of autoregressive integrated moving average and fuzzy time series Markov chain on long-memory data

Devianto

Ramadani

Maiyastri

et al. 2022

Front. Appl. Math. Stat.

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IntroductionThe price of crude oil as an essential commodity in the world economy shows a pattern and identifies the component factors that influence it in the short and long term. The long pattern of the price movement of crude oil is identified by a fractionally time series model where the accuracy can still be improved by making a hybrid residual model using a fuzzy time series approach.MethodsTime series data containing long-memory elements can be modified into a stationary model through the autoregressive fractional integrated moving average (ARFIMA). This fractional model can provide better accuracy on long-memory data than the classic autoregressive integrated moving average (ARIMA) model. The long-memory data are indicated by a high level of fluctuation and the autocorrelation value between lags that decreases slowly. However, a more accurate model is proposed as a hybridization time series model with fuzzy time series Markov chain (FTSMC).ResultsThe time series data collected from the monthly period of West Texas Intermediate (WTI) oil price as the standard for world oil prices for the 2003–2021 time period. The data of WTI oil price has a long-memory data pattern to be modeled fractionally, and subsequently their hybrids. The times series model of crude oil price is obtained as the new target model of hybrid ARIMA and ARFIMA with FTSMC, denoted as ARIMA-FTSMC and ARFIMA-FTSMC, respectively.DiscussionThe accuracy model measured by MAE, RMSE, and MAPE shows that the hybrid model of ARIMA-FTSMC has better performance than ARIMA and ARFIMA, but the hybrid model of ARFIMA-FTSMC provides the best accuracy compared to all models. The superiority of the hybrid time series model of ARFIMA-FTSMC on long-memory data provides an opportunity for the hybrid model as the best and more precise forecasting method.

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The forecasting model of Bitcoin price with fuzzy time series Markov chain and chen logical method

Cited by 12 publications

References 13 publications

Dynamic Type-2 Fuzzy Time Warping (DT2FTW): A Hybrid Model for Uncertain Time-Series Prediction

Dynamic Type-2 Fuzzy Time Warping (DT2FTW): A Hybrid Model for Uncertain Time-Series Prediction

Analysis of Cryptocurrency Market by Using q‐Rung Orthopair Fuzzy Hypersoft Set Algorithm Based on Aggregation Operators

The hybrid model of autoregressive integrated moving average and fuzzy time series Markov chain on long-memory data

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