scholarly journals FORECASTING THE CROP YIELD OF A COCONUT ESTATE

CORD ◽  
1989 ◽  
Vol 5 (02) ◽  
pp. 34
Author(s):  
T. S. G. Peiris
Keyword(s):  
Moving Average ◽  
Day Length ◽  
Physical Factors ◽  
Present Value ◽  
Short Term ◽  
Yield Data ◽  
Autoregressive Integrated Moving Average ◽  
The Past ◽  
Variance Stabilization ◽  
Lower Limits

Seasonal Autoregressive Integrated Moving Average (ARIMA) process of (0,1,2) x (0,1,1) x 6 that best fits a set of crop‑wise coconut yield data, in Bandirippuwa, Lunuwila is identified with­out using variance stabilization transformation. In this process the present value of the series may be described as a linear function of the past observation of the series and past disturbances. The physical factors such as rainfall, temperature, day length etc. are not required for this method, however the past crop figures in the estate is needed. While such model is useful for short term fore­casting, it also gives the upper and lower limits of the forecasts at a given probability. These intervals would provide the quantified information on the degree of duration of the forecasts.

A comparative study of univariate models for container throughput forecasting of major ports in Asia

2021 ◽  
pp. 147509022110236
Author(s):  
Juan Huang ◽  
Ching-Wu Chu ◽  
Hsiu-Li Hsu
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Forecasting Model ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Port Authorities ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Short Term Forecasting ◽  
Trigonometric Regression

This study aims to make comparisons on different univariate forecasting methods and provides a more accurate short-term forecasting model on the container throughput for rendering a reference to relevant authorities. We collected monthly data regarding container throughput volumes for three major ports in Asia, Shanghai, Singapore, and Busan Ports. Six different univariate methods, including the grey forecasting model, the hybrid grey forecasting model, the multiplicative decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, and the seasonal autoregressive integrated moving average (SARIMA) model, were used. We found that the hybrid grey forecasting model outperforms the other univariate models. This study’s findings can provide a more accurate short-term forecasting model for container throughput to create a reference for port authorities.

scholarly journals Forecasting Short-Term Timber Prices with Univariate ARIMA Models

1999 ◽  
Vol 23 (1) ◽  
pp. 53-58 ◽  
Author(s):  
Runsheng Yin
Keyword(s):  
Moving Average ◽  
Arima Models ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Price Series ◽  
Modeling Strategy

Abstract In this paper, we conduct timber price forecasts with univariate autoregressive-integrated-moving-average, or ARIMA, models employing the standard Box-Jenkins modeling strategy. Using quarterly price series from Timber Mart-South, we find that most of the selected pine pulpwood and sawtimber markets can be evaluated using ARIMA models, and that short-term forecasts, especially those of one-lead forecasts, are fairly accurate. We believe that forecasting future prices could aid timber producers and consumers alike in timing harvests, reducing uncertainty, and enhancing efficiency. South. J. Appl. For. 23(1):53-58.

scholarly journals Statistical Forecasting of the Indicators of Polish Airport’s Operations

2012 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Kamil Fijorek ◽  
Agnieszka Leśniewska
Keyword(s):  
Planning Process ◽  
Moving Average ◽  
Short Term ◽  
Airport Management ◽  
Autoregressive Integrated Moving Average ◽  
Transport Aircraft ◽  
Cargo Transport ◽  
Passenger Flows ◽  
Regional Airports ◽  
Crucial Part

Abstract From the perspective of airport management the knowledge of short-term future airport operation levels is a crucial part of the planning process. In this paper we evaluate the forecasting abilities of exponential smoothing (ETS) and seasonal autoregressive integrated moving average (SARIMA) models applied to the monthly time series of cargo transport, aircraft complete operations and passenger flows generated by selected Polish regional airports.

scholarly journals SHORT-TERM TRAFFIC FLOW PREDICTION USING A METHODOLOGY BASED ON AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND GENETIC PROGRAMMING

Transport ◽  
2016 ◽  
Vol 31 (3) ◽  
pp. 343-358 ◽  
Author(s):  
Chengcheng Xu ◽  
Zhibin Li ◽  
Wei Wang
Keyword(s):  
Genetic Programming ◽  
Traffic Flow ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Predictive Performance ◽  
Hybrid Models ◽  
Short Term ◽  
Time Intervals ◽  
Autoregressive Integrated Moving Average

The accurate short-term traffic flow forecasting is fundamental to both theoretical and empirical aspects of intelligent transportation systems deployment. This study aimed to develop a simple and effective hybrid model for forecasting traffic volume that combines the AutoRegressive Integrated Moving Average (ARIMA) and the Genetic Programming (GP) models. By combining different models, different aspects of the underlying patterns of traffic flow could be captured. The ARIMA model was used to model the linear component of the traffic flow time series. Then the GP model was applied to capture the nonlinear component by modelling the residuals from the ARIMA model. The hybrid models were fitted for four different time-aggregations: 5, 10, 15, and 20 min. The validations of the proposed hybrid methodology were performed by using traffic data under both typical and atypical conditions from multiple locations on the I-880N freeway in the United States. The results indicated that the hybrid models had better predictive performance than utilizing only ARIMA model for different aggregation time intervals under typical conditions. The Mean Relative Error (MRE) of the hybrid models was found to be from 4.1 to 6.9% for different aggregation time intervals under typical conditions. The predictive performance of the hybrid method was improved with an increase in the aggregation time interval. In addition, the validation results showed that the predictive performance of the hybrid model was also better than that of the ARIMA model under atypical conditions.

scholarly journals COVID-19 Forecasting: A Statistical Approach

2021 ◽  
pp. 85-96
Author(s):  
Arti Saxena ◽  
Falak Bhardwaj ◽  
Vijay Kumar
Keyword(s):  
Statistical Approach ◽  
Moving Average ◽  
Severe Disease ◽  
Medical Science ◽  
Autoregressive Integrated Moving Average ◽  
The Public ◽  
The Past ◽  
Health Goal ◽  
Public Health Goal ◽  
Very High

Background: SARS-coronavirus-2 is a new virus infecting people and causing COVID-19 disease. The disease is causing a worldwide pandemic. Although some people never develop any signs or symptoms of disease when they are infected, other people are at very high risk for severe disease and death. Objective: If we’re able to intervene to prevent even some transmission, we can dramatically reduce the number of cases. And this is the public health goal for controlling COVID-19. Methods: This article initializes an approach for comparatively accurate values prediction of new cases and deaths for a particular day in order to be considered for preventive measures. The three statistical analysis methods considered for forecasting are Fbprophet, Moving average and the Autoregressive Integrated Moving Average algorithm. Results: The results obtained are in-line with the past and present trend of COVID-19 data collected from WHO website. Conclusion: The output is satisfactory for further consideration. Bangladesh Journal of Medical Science Vol.20(5) 2021 p.85-96

scholarly journals Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique

2018 ◽  
Vol III (IV) ◽  
pp. 413-426
Author(s):  
Mustafa Afeef ◽  
Nazim Ali ◽  
Adnan Khan
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Moving Average ◽  
Stock Market Index ◽  
Short Term ◽  
Average Technique ◽  
Autoregressive Integrated Moving Average ◽  
Past Behavior ◽  
Market Index ◽  
Arima Modeling

Movements in a stock market index may safely be considered one of the mostwatched out phenomena by investors in almost every economy. One method to forecast the index is to study all those external factors that directly affect it. Another way, however, is to base ones predictions on the past behavior of the variable of interest. This paper has employed the method described latter and has, therefore, made use of the ARIMA modeling. In this connection, the daily stock market index data of the Karachi Stock Exchange 100 index was taken for twenty years from 1997 to 2017 which translated into 4940 observations. The study revealed that the model was decently efficient in forecasting the KSE 100 Index, though only for the short-range. The upshot of this study may be utilized specifically by short term investors in deciding on when, and when not, to invest in the stock market.

scholarly journals STUDI PERAMALAN BEBAN RATA – RATA JANGKA PENDEK MENGGUNAKAN METODA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA

Sutet ◽  
2018 ◽  
Vol 7 (2) ◽  
pp. 93-101
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Power Plants ◽  
Time Series Data ◽  
Moving Average ◽  
Current Model ◽  
Series Data ◽  
Electrical Load ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Independent Variable ◽  
Short Term Forecasting

Forecasting. Plans, power plants ,. Electricity needs are increasingly changing daily, so the State Electricity Company (PLN) as a provider of energy must be able to predict daily electricity needs. Short-term forecasting is the prediction of electricity demand for a certain period of time ranging from a few minutes to a week ahead. in shortterm electrical forecasting much of the literature describes the techniques and methods applied in forecasting, Autoregresive Integrated Moving Average (ARIMA), linear regression, and artificial intelligence such as Artificial Neural Networks and fuzzy logic. Short-term forecasting will be done by the authors using time series data that is the data of the use of electric power daily (electrical load) and ARIMA as a method of forecasting. ARIMA method or often called Box-Jenkins technique to find this method is suitable to predict variable costs quickly, simply, and cheaply because it only requires data variables to be predicted. ARIMA can only be used for short-term forecasting. ARIMA is a special linear test, in the form of forecasting this model is completely independent variable variables because this model uses the current model and past values of the dependent variable to produce an accurate short-term forecast.

scholarly journals Spatiotemporal characteristics and the epidemiology of tuberculosis from 2004 to 2017 in China by the nationwide surveillance system

2020 ◽  
Author(s):  
Zhongbao Zuo ◽  
Miaochan Wang ◽  
Huaizhong Cui ◽  
Ying Wang ◽  
Jing Wu ◽  
...  
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Moving Average ◽  
Multivariate Time Series ◽  
Joinpoint Regression ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
The Past ◽  
Leading Role ◽  
Joinpoint Regression Analysis

Abstract BackgroundChina has always been one of the countries with the most serious tuberculosis epidemic in the world. Our study was to observe the Spatial-temporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model.MethodsThe data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the Chinese Center for Disease Control and Prevention. The incidence trend of TB was observed by the Joinpoint regression analysis. The Seasonal autoregressive integrated moving average (SARIMA) model was used to predict the monthly incidence. Geographic clusters was employed to analyze the Spatial autocorrelation analysis was performed to detect. The heterogeneous transmission of TB was detected by the multivariate time series model. ResultsWe included 13,991,850 TB cases from January 2004 to December 2017, with a yearly average morbidity of 999,417 cases. The final selected model was the 0 Joinpoint model (P=0.0001) with an annual average percent change (AAPC) of -3.3 (95% CI: -4.3 to -2.2, P<0.001). A seasonality was observed across the fourteen years, and the seasonal peaks were in January and March every year. The best SARIMA model was (0, 1, 1) X (0, 1, 1)12 which can be written as (1-B) (1-B12) Xt = (1-0.42349B) (1-0.43338B12) εt, with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The MSE, RMSE, MAE, and MAPE of the modelling performance were 201.76, 14.2, 8.4 and 0.06, respectively. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The hotspot of TB transmission was mainly located at southern region of China from 2004 to 2008, including Hainan, Guangxi, Guizhou, and Chongqing, which disappeared in the later years. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5% - 84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences (>70 cases per 100,000) were influenced by the autoregressive component for the past fourteen years. ConclusionIn a word, China still has a high TB incidence. However, the incidence rate of TB was significantly decreasing from 2004 to 2017 in China. Seasonal peaks were in January and March every year. Obvious geographical clusters were observed in Tibet and Xinjiang Province. The spatial heterogeneity of TB driving transmission was distinguished from the multivariate time series model. For every provinces over the past fourteen years, the autoregressive component played a leading role in the incidence of TB which need us to enhance the early protective implementation.

Deep learning-based container throughput forecasting: a triple bottom line approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonali Shankar ◽  
Sushil Punia ◽  
P. Vigneswara Ilavarasan
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
Short Term ◽  
Bottom Line ◽  
Content Type ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method ◽  
Memory Network ◽  
Long Short Term Memory

PurposeContainer throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of container throughput are observed to enhance the predicting accuracy. Therefore, for effective port planning and management, this study employs a deep learning-based method to forecast the container throughput while considering the influence of economic, environmental and social factors on throughput forecasting.Design/methodology/approachA novel multivariate container throughput forecasting method is proposed using long short-term memory network (LSTM). The external factors influencing container throughput, delineated using triple bottom line, are considered as an input to the forecasting method. The principal component analysis (PCA) is employed to reduce the redundancy of the input variables. The container throughput data of the Port of Los Angeles (PLA) is considered for empirical analysis. The forecasting accuracy of the proposed method is measured via an error matrix. The accuracy of the results is further substantiated by the Diebold-Mariano statistical test.FindingsThe result of the proposed method is benchmarked with vector autoregression (VAR), autoregressive integrated moving average (ARIMAX) and LSTM. It is observed that the proposed method outperforms other counterpart methods. Though PCA was not an integral part of the forecasting process, it facilitated the prediction by means of “less data, more accuracy.”Originality/valueA novel deep learning-based forecasting method is proposed to predict container throughput using a hybridized autoregressive integrated moving average with external factors model and long short-term memory network (ARIMAX-LSTM).

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