Research Article
ETS - ARIMA Intervention Modelling of Bangladesh Taka/Nigerian Naira Exchange Rates
Elisha John Inyang*,
Ngia Matthew Nafo,
Anthony Ike Wegbom,
Yvonne Asikiye Da-Wariboko
Issue:
Volume 12, Issue 1, February 2024
Pages:
1-12
Received:
20 November 2023
Accepted:
18 December 2023
Published:
11 January 2024
Abstract: In real-world scenarios, numerous external events disrupt many time series, causing fluctuations in the series' mean level. When modeling such series using the traditional ARIMA model, this can result in distortions in the model's parameter estimations, the structure of the fitted model, and future value projections. Any unusual values in the series that might have arisen as a result of the special event could be adjusted using the Box-Tiao intervention modeling technique. This study investigates time series intervention modelling based on ETS and ARIMA models aimed at studying the response of the comparative value of the Bangladesh Taka to the Nigerian Naira due to the 2016 economic recession. The dataset for this study is the daily exchange rate of Bangladesh Taka to Nigerian Naira from January to December 2016. The BDT/NGN2016 exchange rates have been considered, with a step intervention being the introduction of the economic recession in June 2016. Results revealed an initial impact of 0.5217. The intervention caused a 68.49% depreciation in the value of the Naira exchanged with the Bangladesh Taka in the exchange rate market, with a decay rate of 0.6. The intervention effect was persistent, with a long-run effect of 1.2862. Hence, the intervention had a gradual start and a permanent effect.
Abstract: In real-world scenarios, numerous external events disrupt many time series, causing fluctuations in the series' mean level. When modeling such series using the traditional ARIMA model, this can result in distortions in the model's parameter estimations, the structure of the fitted model, and future value projections. Any unusual values in the serie...
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Research Article
Optimization of the Non-Linear Diffussion Equations
Issue:
Volume 12, Issue 1, February 2024
Pages:
13-19
Received:
27 February 2024
Accepted:
12 March 2024
Published:
2 April 2024
DOI:
10.11648/j.sjams.20241201.12
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Abstract: Partial Differential Equations are used in smoothening of images. Under partial differential equations an image is termed as a function; f(x, y), XÎR2. The pixel flux is referred to as an edge stopping function since it ensures that diffusion occurs within the image region but zero at the boundaries; ux(0, y, t) = ux(p, y, t) = uy(x, 0, t) = uy(x, q, t). Nonlinear PDEs tend to adjust the quality of the image, thus giving images desirable outlooks. In the digital world there is need for images to be smoothened for broadcast purposes, medical display of internal organs i.e MRI (Magnetic Resonance Imaging), study of the galaxy, CCTV (Closed Circuit Television) among others. This model inputs optimization in the smoothening of images. The solutions of the diffusion equations were obtained using iterative algorithms i.e. Alternating Direction Implicit (ADI) method, Two-point Explicit Group Successive Over-Relaxation (2-EGSOR) and a successive implementation of these two approaches. These schemes were executed in MATLAB (Matrix Laboratory) subject to an initial condition of a noisy images characterized by pepper noise, Gaussian noise, Brownian noise, Poisson noise etc. As the algorithms were implemented in MATLAB, the smoothing effect reduced at places with possibilities of being boundaries, the parameters Cv (pixel flux), Cf (coefficient of the forcing term), b (the threshold parameter) alongside time t were estimated through optimization. Parameter b maintained the highest value, while Cv exhibited the lowest value implying that diffusion of pixels within the various images i.e. CCTV, MRI & Galaxy was limited to enhance smoothening. On the other hand the threshold parameter (b) took an escalated value across the images translating to a high level of the force responsible for smoothening.
Abstract: Partial Differential Equations are used in smoothening of images. Under partial differential equations an image is termed as a function; f(x, y), XÎR2. The pixel flux is referred to as an edge stopping function since it ensures that diffusion occurs within the image region but zero at the boundaries; ux(0, y, t) = ux(p, y, t) = uy(x, 0, t) = uy(x, ...
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,
Isaac Chepkwony
