Trend forecasting of financial time series using PIPs detection and continuous HMM

Park, Sang-Ho; Lee, Ju-Hong; Lee, Hyo-Chan

doi:10.3233/ida-2011-0495

Cited by 3 publications

(1 citation statement)

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“…HMMs applications are now being extended to many fields such as traffic congestion [3], dynamic system modeling and diagnosis [4], neurosciences [5], stock market [1,6,7,8,9,10,11,12], inflation analysis [13], safety messages [14] and exchange rate [15, 16]and etc. Hidden Markov Model basically is a statistical model that has been proven useful in wide range applications domain.…”

Section: Introuctionmentioning

confidence: 99%

A novel approach of hidden Markov model for time series forecasting

Zahari

Jaafar

2015

Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

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In the past few years, tremendous studies have been made to examine the accuracy of time series forecasting that provide the foundation for decision models in foreign exchange data. This study proposes a novel approach of Hidden Markov Model and Case Based reasoning for time series forecasting. This paper compares the proposed method with the single HMM and HMM ensemble with neural network. HMM is trained by using forward-backward or Baum-Welch algorithm and the likelihood value is used to predict future exchange rate price. The forecasting accuracy has been measured according to Root Mean Square Error (RMSE). The statistical performance of all techniques is investigated in testing of EUR/USD exchange rate time series over the period of October 2010 to March 2014. The preliminary results indicate that the new approach of HMM produce the lowest RMSE compared to the benchmark models. Further study is to adopt HMM-CBR in testing of GBP/USD, GBP/JPY, USD/JPY, and EUR/JPY exchange rate. KeywordsHidden Markov Model, Case Based Reasoning, Baum-Welch algorithm, Ibk algorithm. INTROUCTIONForecasting time series has been one of the most challenging tasks due to non-linear in nature. Hidden Markov Model (HMM) plays a key role to find the non-linear patterns [1].Over the past few decades, HMM has been widely applied as a data-driven modeling approach in automatic speech recognition [2].HMMs applications are now being extended to many fields such as traffic congestion [3], dynamic system modeling and diagnosis [4], neurosciences [5], stock market [1,6,7,8,9,10,11,12], inflation analysis [13], safety messages [14] and exchange rate [15, 16]and etc. Hidden Markov Model basically is a statistical model that has been proven useful in wide range applications domain. On the other hand their several limitations and mix results regarding their forecasting rule are creating vagueness about their use in real exchange rate trading environment. This scenario is promoted by the fact that the initialization of model parameters and inputs is difficult because of many factors that leads to diverse conclusions. According to [16] HMM are unstable to be taken in as a trading tool on foreign exchange data with too many factors influencing the results [15]. Diana Roman and Gautam Mitra in [20] consider estimating the parameters of an HMM is a difficult task. While implementing the HMM, the choice of the model, choice of the number of states and observation symbol (continuous or discrete or multi-mixture) become a tedious task [6]. Continuous Hidden Markov Model has high calculation complexity problem [10].This paper is to establish a new approach of Hidden Markov Model which attempt to overcome some of the limitations from the literature. In particular the proposed method is to reduce the numbers of parameters while on the other hand it increases the forecasting ability of the model. The study used the well established technique of HMM in a new way to forecast EUR/USD currency pair. In this paper, the first step is to establish pattern from the p...

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Section: Introuctionmentioning

confidence: 99%

A novel approach of hidden Markov model for time series forecasting

Zahari

Jaafar

2015

Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

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Combining Hidden Markov Model and Case Based Reasoning for Time Series Forecasting

Zahari

Jaafar

2015

Communications in Computer and Information Science

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Abstract. Hidden Markov Model is one of the most popular and broadly used for representation vastly structured series of data. This paper presents the application of the new approach of Hidden Markov Model and three ensemble nonlinear models to forecasting the foreign exchange rates. The proposed approach and other combination of computational intelligent techniques such as multi layer perceptron, support vector machine are compared with root mean squared error (RMSE) and Mean Absolute Error (MAE) as the performance measures. The results indicate that the new approach of Hidden Markov Model yield the best results consistently over all the currencies. and Case Based Reasoning based ensembles Based on the numerical experiments conducted, it is inferred that using the correct sophisticated ensemble methods in the computational intelligence paradigm can enhance the results obtained by the extent techniques to forecast foreign exchange rates. This suggests that the new approach of HMM is a powerful analytical instrument that is satisfactorily compared to using only the single model and other soft computing techniques for exchange rate predictions.

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