scholarly journals Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model

2018 ◽  
Vol 18 (1) ◽  
pp. 70-82
Author(s):  
Ernest Kissi ◽  
Theophilus Adjei-Kumi ◽  
Peter Amoah ◽  
Jerry Gyimah
Keyword(s):  
Price Index ◽  
Predictive Accuracy ◽  
Early Stage ◽  
Moving Average ◽  
Predictive Ability ◽  
Integrated Modelling ◽  
Final Decision ◽  
Exogenous Variables ◽  
Vector Error Correction ◽  
Autoregressive Integrated Moving Average

Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI) is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX) in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics.

scholarly journals Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 580-595
Author(s):  
Apostolos Ampountolas
Keyword(s):  
Neural Networks ◽  
Predictive Accuracy ◽  
Moving Average ◽  
Demand Forecasting ◽  
Garch Models ◽  
Exogenous Variables ◽  
Autoregressive Integrated Moving Average ◽  
Accuracy Measure ◽  
Modeling And Forecasting ◽  
Mlp Model

Overnight forecasting is a crucial challenge for revenue managers because of the uncertainty associated between demand and supply. However, there is limited research that focuses on predicting daily hotel demand. Hence, this paper evaluates various models’ of traditional time series forecasting performances for daily demand at multiple horizons. The models include the seasonal naïve, Holt–Winters (HW) triple exponential smoothing, an autoregressive integrated moving average (ARIMA), a seasonal autoregressive integrated moving average (SARIMAX) with exogenous variables, multilayer perceptron (MLP) artificial neural networks model (ANNs), an sGARCH, and GJR-GARCH models. The dataset of this study contains daily demand observations from a hotel in a US metropolitan city from 2015 to 2019 and a set of exogenous social and environmental features such as temperature, holidays, and hotel competitive set ranking. Experimental results indicated that under the MAPE accuracy measure: (i) the SARIMAX model with external regressors outperformed the ANN-MLP model with similar external regressors and the other models, in every one horizon except one out of seven forecast horizons; (ii) the sGARCH(4, 2) and GJR-GARCH(4, 2) shows a superior predictive accuracy at all horizons. The results performance is evaluated by conducting pairwise comparisons between the different model’s distribution of forecasts using Diebold–Mariano and Harvey–Leybourne–Newbold tests. The results are significant for revenue managers because they provide valuable insights into the exogenous variables that impact accurate daily demand forecasting.

scholarly journals Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

2021 ◽  
Vol 8 ◽  
Author(s):  
Veerasak Punyapornwithaya ◽  
Katechan Jampachaisri ◽  
Kunnanut Klaharn ◽  
Chalutwan Sansamur
Keyword(s):  
Milk Production ◽  
Hybrid Model ◽  
Moving Average ◽  
Seasonal Fluctuation ◽  
Predictive Ability ◽  
Northern Region ◽  
Average Error ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Forecast ◽  
Forecast Models

Milk production in Thailand has increased rapidly, though excess milk supply is one of the major concerns. Forecasting can reveal the important information that can support authorities and stakeholders to establish a plan to compromise the oversupply of milk. The aim of this study was to forecast milk production in the northern region of Thailand using time-series forecast methods. A single-technique model, including seasonal autoregressive integrated moving average (SARIMA) and error trend seasonality (ETS), and a hybrid model of SARIMA-ETS were applied to milk production data to develop forecast models. The performance of the models developed was compared using several error matrices. Results showed that milk production was forecasted to raise by 3.2 to 3.6% annually. The SARIMA-ETS hybrid model had the highest forecast performances compared with other models, and the ETS outperformed the SARIMA in predictive ability. Furthermore, the forecast models highlighted a continuously increasing trend with evidence of a seasonal fluctuation for future milk production. The results from this study emphasizes the need for an effective plan and strategy to manage milk production to alleviate a possible oversupply. Policymakers and stakeholders can use our forecasts to develop short- and long-term strategies for managing milk production.

scholarly journals Time Series Modelling of Inflation in Botswana Using Monthly Consumer Price Indices

2016 ◽  
Vol 8 (3) ◽  
pp. 15
Author(s):  
Kesaobaka Molebatsi ◽  
Mpho Raboloko
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Price Index ◽  
Moving Average ◽  
Consumer Price Index ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Modelling ◽  
Error Terms ◽  
Autoregressive Conditional Heteroscedasticity

<p>This paper identifies an autoregressive integrated moving average (ARIMA (1,1,1)) model that can be used to model inflation measured by the consumer price index (CPI) for Botswana. The paper proceeds to improve the model by incorporating the generalized autoregressive conditional heteroscedasticity (ARCH/GARCH) model that takes into consideration volatility in the series. Ultimately, CPI is forecast using the two models, ARIMA (1, 1, 1) and ARIMA (1, 1, 1) + GARCH (1, 2) and compared with the actual CPI. Both models perform well in terms of forecasting as their 95 percent confidence intervals cover the actual CPI. Marginal differences that favour the inclusion of the ARCH/GARCH components were observed when testing for normality among error terms. The paper also reveals that volatility for Botswana’s CPI is low as shown by small values of ARCH/GARCH components.</p>

Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers

2020 ◽  
Vol 21 (10) ◽  
pp. 1008-1025
Author(s):  
A. Gouri ◽  
B. Benarba ◽  
A. Dekaken ◽  
H. Aoures ◽  
S. Benharkat
Keyword(s):  
Breast Cancer ◽  
Distant Metastasis ◽  
Predictive Accuracy ◽  
Early Stage ◽  
Predictive Ability ◽  
Late Recurrence ◽  
Lymph Node Status ◽  
Risk Of Recurrence ◽  
Increased Risk ◽  
Hormonal Receptor Status

Recently, a significant number of breast cancer (BC) patients have been diagnosed at an early stage. It is therefore critical to accurately predict the risk of recurrence and distant metastasis for better management of BC in this setting. Clinicopathologic patterns, particularly lymph node status, tumor size, and hormonal receptor status are routinely used to identify women at increased risk of recurrence. However, these factors have limitations regarding their predictive ability for late metastasis risk in patients with early BC. Emerging molecular signatures using gene expression-based approaches have improved the prognostic and predictive accuracy for this indication. However, the use of their based-scores for risk assessment has provided contradictory findings. Therefore, developing and using newly emerged alternative predictive and prognostic biomarkers for identifying patients at high- and low-risk is of great importance. The present review discusses some serum biomarkers and multigene profiling scores for predicting late recurrence and distant metastasis in early-stage BC based on recently published studies and clinical trials.

scholarly journals Trend Analysis in Sugarcane Growth in Mumias Sugar Belt, Western Kenya; for the Period 1985-2015

2021 ◽  
Vol 3 (1) ◽  
pp. 31-40
Author(s):  
Protas Khaemba ◽  
PHILOMENA MUIRURI ◽  
THOMAS KIBUTU
Keyword(s):  
Trend Analysis ◽  
Management Practices ◽  
Moving Average ◽  
Secondary Data ◽  
Predictive Ability ◽  
Forecast Period ◽  
Western Kenya ◽  
Autoregressive Integrated Moving Average ◽  
Sugarcane Varieties ◽  
Good Agreement

The study was carried out to examine trends in the output and acreage in the Mumias Sugar belt from the period 1985-2015. We used secondary data collected from Mumais Sugar Company records for the period 1985-2015 for the study. The trend analysis of sugarcane production in the Mumias Sugar Belt is important, where sugarcane is the major cash crop and absorbs a majority of the agrarian population in the region. The study used the expert modeler, an autoregressive integrated moving average (ARIMA), to predict the output. The forecast period was 2016 through March 2021 and employed two scenarios: I) forecast with +2 harvesting age predictor modification and ii) forecast with +10 hectares predictor modification. The predicted value showed good agreement with the observed values from the series plot, indicating that the model has a good predictive ability. The application of the model revealed that the results in the prediction tables show that, in each of the six forecasted quarters, increasing the harvesting age by two months is expected to generate about 4.52 more tons of yields per hectare than increasing area harvested by 10 hectares that would decrease the yield by 0.01 tons per hectare. The study recommends research and development on sugarcane varieties that mature early, making sugarcane-based Agri- enterprises and sustainable. In addition, Mumias Sugar Company should seek profitable techniques to increase the recovery per cent, and farmers seek good management practices to increase the efficiency of the sugarcane farms in the sugar belt.

Komparasi Regresi Ekonometri pada Perekonomian Indonesia 2SLS, VEC, dan ARIMA

2002 ◽  
Vol 2 (2) ◽  
pp. 88-112
Author(s):  
Henry Viriya Surya ◽  
Prastowo Cahjadi
Keyword(s):  
Time Series ◽  
Regression Models ◽  
Moving Average ◽  
Strong Support ◽  
Comparison Process ◽  
Economic Time Series ◽  
Vector Error Correction ◽  
Autoregressive Integrated Moving Average ◽  
Vector Error ◽  
Economic Time

This paper compares three models of econometric analysis on economy, in this case the Indonesian economy. The regression models are the two stage least squares (2SLS) which has a strong support from the economic theory of aggregate expenditure, the Vector Error Correction (VEC) and Autoregressive Integrated Moving Average (ARIMA) which both comes from the time series analysis, that do not have to be economic time series. The study tries to find out which are most suitable in analyzing the time series of Indonesian economy. After all the estimation and comparison process, we finally agree that the use of those different methods must be sinchronized with the purpose of the user's study of the economic time series.

scholarly journals Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models

2021 ◽  
Vol 2 (6) ◽  
pp. 50-63
Author(s):  
Teddy Mutugi Wanjuki ◽  
Adolphus Wagala ◽  
Dennis K. Muriithi
Keyword(s):  
Monetary Policy ◽  
Fiscal Policy ◽  
Price Index ◽  
Moving Average ◽  
Estimation Method ◽  
Series Data ◽  
Policy Objective ◽  
Autoregressive Integrated Moving Average ◽  
Food And Beverages ◽  
Sarima Models

Price stability is the primary monetary policy objective in any economy since it protects the interests of both consumers and producers. As a result, forecasting is a common practice and a vital aspect of monetary policymaking. Future predictions guide monetary and fiscal policy tools that that be used to stabilize commodity prices. As a result, developing an accurate and precise forecasting model is critical. The current study fitted and forecasted the food and beverages price index (FBPI) in Kenya using seasonal autoregressive integrated moving average (SARIMA) models. Unlike other ARIMA models like the autoregressive (AR), Moving Average (MA), and non-seasonal ARMA models, the SARIMA model accounts for the seasonal component in a given time series data better forecasts. The study relied on secondary data obtained from the KNBS website on monthly food and beverage price index in Kenya from January 1991 to February 2020. R-statistical software was used to analyze the data. The parameter estimation was done using the Maximum Likelihood Estimation method. Competing SARIMA models were compared using the Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE),.and Mean Absolute Percentage Error (MAPE). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The forecasting ability evaluation statistics MAE = 2.00%, MAPE = 1.62% and MASE = 0.87%. The 24-step ahead forecasts showed that the FPBI is unstable with an overall increasing trend. Therefore, the monetary policy committee ought to control inflation through monetary or fiscal policy, strengthening food security and trade liberalization.

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