Time series modeling of the interaction between deterministic and stochastic trends

Moffat, Imoh Udo; Akpan, Emmanuel Alphonsus

doi:10.21833/ijaas.2017.09.012

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…In Statistics, trends are steady and methodical fluctuations in the variance of a process over a long period of time and their underlying features are embedded with useful information that are viable for investment decisions [1][2][3][4]. The uncertainty or probabilistic nature of stock investment has made trend analysis a potential and promising tool for investors, who seek capital appreciation, hence, the quest for an appropriate model that could offer the best prediction.…”

Section: Introductionmentioning

confidence: 99%

“…However, no previous studies have attempted to take into consideration the effect of changing variance while analyzing investment trends through Markov chains. It is evident in the literature that stocks trends are characterized by changing variance [16][17][18][19][20][21][22][23]. The consequential effect of neglecting changing variance is that it takes longer time to achieve convergence of the probabilities of Markov chain.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach

Moffat¹,

Ukpabio²,

Akpan³

2020

JSTA

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The study aimed at stabilizing the changing variance using the logarithmic transformation to achieve a significant proportion of stability and a faster rate of convergence of the steady state transition probability in Markov chains. The traditional Markov chain and logarithmic-modified Markov chain were considered. On exploring the yearly data on the stock prices from 2015 to 2018 as obtained from the Nigerian Stock Exchange, it was found that the steady state of logarithmic-modified Markov chain converged faster than the tradition Markov chain with efficiency in tracking the correct cycles where the stock movements are trending irrespective of which cycle it starts at time zero with differences in probability values by 1.1%, 0.7%, −0.41% and −1.37% for accumulation, markup, distribution and mark-down cycles, respectively. Thus, it could be deduced that the logarithmic modification enhances the ability of the Markov chain to tract the variation of the steady state probabilities faster than the traditional counterpart.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach

Moffat¹,

Ukpabio²,

Akpan³

2020

JSTA

Self Cite

View full text Add to dashboard Cite

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Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models

Mustaffa,

Zahari,

Farhana

et al. 2024

Int. j. adv. appl. sci.

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Currently, no vaccines or specific treatments are available to treat or prevent the increasing incidence of dengue worldwide. Therefore, an accurate prediction model is needed to support the anti-dengue control strategy. The primary objective of this study is to develop the most accurate model to predict future dengue cases in the Malaysian environment. This study uses secondary data collected from the weekly reports of the Ministry of Health Malaysia (MOH) website over six years, from 2017 to 2022. Three forecasting techniques, including seasonal autoregressive integrated moving average (SARIMA), dynamic harmonic regression (DHR), and neural network autoregressive model (NNAR), were first fitted to the estimation part of the data. First, several SARIMA models were run, and the best seasonal model identified was SARIMA (0, 1, 2) (1, 1, 1)52. The best DHR model was obtained with a Fourier term of 2, as this corresponds to the lowest Akaike Information Criteria (AIC) value. The NNAR (9, 1, 6)52 was considered the best choice among the NNAR models due to its superior performance in terms of the lowest error measures. The comparison among the three techniques revealed that the DHR model was the best due to its lowest MAPE and RMSE values. Thus, the DHR model was used to generate future forecasts of weekly dengue cases in Malaysia until 2023. The results showed that the model predicted more than a thousand dengue cases around weeks 27 to 32. The results showed an increase in dengue cases after the end of the monsoon season, which lasted about five months. This technique is proving to be valuable for health administrators in improving preparedness.

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Time series modeling of the interaction between deterministic and stochastic trends

Cited by 2 publications

References 9 publications

Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach

Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach

Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models

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