Forecasting Malaysian Ringgit Using Exponential Smoothing Techniques

Forecasting the foreign currency exchange is a challenging task since it is influenced by political, economic and psychological factors. This paper focuses on the forecasting Malaysian Ringgit (MYR) exchange rate against the United States Dollar (USD) using Exponential Smoothing Techniques which are Single Exponential Smoothing, Double Exponential Smoothing, and Holt’s method. The objectives of this paper are to identify the best Exponential Smoothing Technique that describes MYR for 5 years period and to forecast MYR 12 months ahead by using the best Exponential Smoothing Technique. The comparison between these techniques is also made and the best one will be selected to forecast the MYR exchange rate against USD. The result showed that Holt’s method has the smallest value of error measure which depending on the Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) for the evaluation part. The MSE is 1.43915x10-14 and MAPE is 2.5413 x 10-6. Meanwhile, the forecast value of MYR in August 2019 is RM 4.30226.

Applicability of Smoothing Techniques in Generation of Phenological Metrics of Tectona grandis L. Using NDVI Time Series Data

Information on phenological metrics of individual plant species is meager. Phenological metrics generation for a specific plant species can prove beneficial if the species is ecologically or economically important. Teak, a dominating tree in most regions of the world has been focused on in the present study due to its multiple benefits. Forecasts on such species can attain a substantial improvement in their productivity. MODIS NDVI time series when subjected to statistical smoothing techniques exhibited good output with Tukey’s smoothing (TS) with a low RMSE of 0.042 compared to single exponential (SE) and double exponential (DE). Phenological metrics, namely, the start of the season (SOS), end of the season (EOS), maximum of the season (MAX), and length of the season (LOS) were generated using Tukey-smoothed MODIS NDVI data for the years 2003–2004 and 2013–2014. Post shifts in SOS and EOS by 14 and 37 days respectively with a preshift of 28 days in MAX were observed in the year 2013–2014. Preshift in MAX was accompanied by an increase in greenness exhibiting increased NDVI value.LOS increased by 24 days in the year 2013–2014, showing an increase in the duration of the season of teak. Dates of these satellite-retrieved phenological occurrences were validated with ground phenological data calculated using crown cover assessment. The present study demonstrated the potential of a spatial approach in the generation of phenometrics for an individual plant species, which is significant in determining productivity or a crucial trophic link for a given region.

A Comparative Study of Models for the Construction Duration Prediction in Highway Road Projects of India

Predicting the duration of construction projects with acceptable accuracy is a problem for contractors and researchers. Numerous researchers and tools are involved in sorting out this problem. The aim of the study is to predict the construction duration using four analytical tools as an approach. The success of construction projects in regard to time depends on various factors such as selection of contractors, consultants, cost of the projects, quality of the projects, the quantity of the projects, environmental factors, etc. Presently available commercial tools in the market are not designed as universally common and concerned. Every tool performs well in a particular situation. The prediction of India’s highway road projects duration is the biggest construction issue in the country due to various reasons. To overcome this problem, the methodology of the paper adopts various strategies to find suitable tools to predict the highway road projects’ duration, in which it classifies and analyzes the collected data. As a part of this work, the details of 363 government infrastructure projects (traditional procurement) were collected from 2000 to 2018. The present study also adopts various tools for duration prediction such as artificial neural networks (ANNs), smoothing techniques, time series analysis, and Bromilow’s time–cost (BTC) model. The results of the study recommend smoothing techniques with a constant value of 0.3, which gave the remarkable very small error of 1.2%, and its outcomes become even better when compared to other techniques.

Volatility forecast of CIDC Construction Cost Index using smoothing techniques and machine learning

The Construction Industry Development Council (CIDC) of India has been calculating and publishing the Construction Cost Index (CCI), monthly, since 1998. Construction cost variations interrogate different kinds of projects such as roads, power plants, buildings, industrial structures, railways and bridges. The success rate of completion of construction project is diminished due to the lack of prediction knowledge in CCI. Predicting CCI in greater accuracy is quite difficult for contractor and academicians. The following factors are influenced higher in CCI such as population, unemployment rate, consumer price index (CPI), long term interest rate, domestic credit growth, Gross Domestic Product (GDP) and money supply (M4). CCI can be used to forecast the construction cost. The relevant resource data was collected across the nation between 2003 and 2018. As outcome-based, non-econometric tools such as smoothing techniques, artificial neural network (ANN) and support vector machines (SVMs) have produced a better outcome. Among these, smoothing techniques have given the notable low error and high accuracy. This accuracy has measured by Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The major objective of this research is to help the cost estimator to avoid underestimation and overestimation.

Collapsing Geometry with Ricci Curvature Bounded Below and Ricci Flow Smoothing

We survey some recent developments in the study of collapsing Riemannian manifolds with Ricci curvature bounded below, especially the locally bounded Ricci covering geometry and the Ricci flow smoothing techniques. We then prove that if a Calabi-Yau manifold is sufficiently volume collapsed with bounded diameter and sectional curvature, then it admits a Ricci-flat Kähler metrictogether with a compatible pure nilpotent Killing structure: this is related to an open question of Cheeger, Fukaya and Gromov.