scholarly journals Forecasting Wheat Production in Pakistan

2008 ◽  
Vol 13 (1) ◽  
pp. 57-85 ◽  
Author(s):  
Falak Sher ◽  
Eatzaz Ahmad
Keyword(s):  
Production Function ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Forecasting Model ◽  
Future Prospects ◽  
Wheat Production ◽  
Steady Growth ◽  
Forecasting Performance ◽  
The Future ◽  
Out Of Sample

This study analyzes the future prospects of wheat production in Pakistan. Parameters of the forecasting model are obtained by estimating a Cobb-Douglas production function for wheat, while future values of various inputs are obtained as dynamic forecasts on the basis of separate ARIMA estimates for each input and for each province. Input forecasts and parameters of the wheat production function are then used to generate wheat forecasts. The results of the study show that the most important variables for predicting wheat production per hectare (in order of importance) are: lagged output, labor force, use of tractors, and sum of the rainfall in the months of November to March. The null hypotheses of common coefficients across provinces for most of the variables cannot be rejected, implying that all variables play the same role in wheat production in all the four provinces. Forecasting performance of the model based on out-of-sample forecasts for the period 2005-06 is highly satisfactory with 1.81% mean absolute error. The future forecasts for the period of 2007-15 show steady growth of 1.6%, indicating that Pakistan will face a slight shortage of wheat output in the future.

Forecasting Malaysian overnight islamic interbank rate using the Box-Jenkins model

2021 ◽  
Vol 2 (1) ◽  
pp. 38-51
Author(s):  
N.S.M. Radzi ◽  
S.R. Yaziz
Keyword(s):  
Monetary Policy ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Prediction Intervals ◽  
Percentage Error ◽  
Mean Square ◽  
Islamic Banks ◽  
Financial Instruments ◽  
Policy Makers ◽  
Forecasting Performance

Modelling the overnight Islamic interbank rate (IIR) is imperative to define the IIR performance as it would help the Islamic banks to adjust its costs of funding effectively and facilitate the policy makers to regulate a comprehensive monetary policy in Malaysia. The IIR framework which has been regulated by Bank Negara Malaysia under dual banking and financial system has always been overlooked in most previous studies in modelling the financial instruments rates. Therefore, it is vital to select the appropriate model as it resembles with the features of the IIR. The study assesses the forecasting performance of overnight IIR using the Box-Jenkins model. The suggested Box-Jenkins model has been applied to the Malaysian overnight IIR (in percentage) from 02/01/2001 to 31/12/2020. The empirical results determine that ARIMA (0,1,1) is the most appropriate model in forecasting overnight IIR as the model provides the smallest Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). In multistep ahead forecasting, it can be summarised that ARIMA (0,1,1) model is able to trail the actual data trend of daily Malaysian overnight IIR up to 5-day ahead within 95% prediction intervals.

scholarly journals Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1621 ◽  
Author(s):  
Alfredo Nespoli ◽  
Emanuele Ogliari ◽  
Sonia Leva ◽  
Alessandro Massi Pavan ◽  
Adel Mellit ◽  
...  
Keyword(s):  
Hybrid Method ◽  
Mean Absolute Error ◽  
Weather Forecast ◽  
Absolute Error ◽  
The Other ◽  
Pv System ◽  
Weather Forecasts ◽  
Forecasting Method ◽  
Forecasting Performance ◽  
Stable Performance

We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% < NMAE% < 2%); the hybrid method shows an even better performance (NMAE% < 1%) for two of the days considered in this analysis, but overall a less stable performance (NMAE% > 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.

scholarly journals APLIKASI ALGORITMA GENETIKA UNTUK MERAMALKAN KONSUMSI PREMIUM KOTA DENPASAR

2014 ◽  
Vol 3 (4) ◽  
pp. 160
Author(s):  
VICTOR MALLANG ◽  
KETUT JAYANEGARA ◽  
NI MADE ASIH ◽  
I PUTU EKA N. KENCANA
Keyword(s):  
Mean Absolute Error ◽  
Fitness Function ◽  
Absolute Error ◽  
Series Data ◽  
Acceptable Solution ◽  
Gasoline Demand ◽  
Out Of Sample ◽  
Genetic Algorithm Method ◽  
Partial Autocorrelation Function ◽  
Six Genes

This research aimed to forecast the gasoline demand at Denpasar using genetic algorithm method. This  algorithm was selected because of easy to implement and its ability to find acceptable solution quickly.  This algorithm works by searching the best individu according to fitness function defined. The series data used in the research were 60 observations of monthly gasoline demand at Denpasar for period January 2009 through December 2013.  By observing the Partial Autocorrelation Function (PACF) plot, we found the last lag before the series become stationer was sixth lag.  Based on this finding, we decided the best individu was represented by six genes. This individu, in addition, was used to make in-sample forecasting.  The forecasted data had mean absolute error (MAE) as much as 553,27 kiloliters.  For one semester out-of sample forecast, we found gasoline consumption fluctuated with lowest and highest consumption were for February 2014 and June 2014, respectively.

scholarly journals Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China

2020 ◽  
Author(s):  
Chiou-Jye Huang ◽  
Yung-Hsiang Chen ◽  
Yuxuan Ma ◽  
Ping-Huan Kuo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Forecasting Model ◽  
Cumulative Number ◽  
Mean Square ◽  
Multiple Input ◽  
Network Method

AbstractCOVID-19 is spreading all across the globe. Up until March 23, 2020, the confirmed cases in 173 countries and regions of the globe had surpassed 346,000, and more than 14,700 deaths had resulted. The confirmed cases outside of China had also reached over 81,000, with over 3,200 deaths. In this study, a Convolutional Neural Network (CNN) was proposed to analyze and predict the number of confirmed cases. Several cities with the most confirmed cases in China were the focus of this study, and a COVID-19 forecasting model, based on the CNN deep neural network method, was proposed. To compare the overall efficacies of different algorithms, the indicators of mean absolute error and root mean square error were applied in the experiment of this study. The experiment results indicated that compared with other deep learning methods, the CNN model proposed in this study has the greatest prediction efficacy. The feasibility and practicality of the model in predicting the cumulative number of COVID-19 confirmed cases were also verified in this study.

scholarly journals Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 325
Author(s):  
Adriana AnaMaria Davidescu ◽  
Simona-Andreea Apostu ◽  
Andreea Paul
Keyword(s):  
Unemployment Rate ◽  
Mean Squared Error ◽  
Moving Average ◽  
Model Identification ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Forecast Horizon ◽  
Forecasting Performance ◽  
Sample Data ◽  
Out Of Sample

Unemployment has risen as the economy has shrunk. The coronavirus crisis has affected many sectors in Romania, some companies diminishing or even ceasing their activity. Making forecasts of the unemployment rate has a fundamental impact and importance on future social policy strategies. The aim of the paper is to comparatively analyze the forecast performances of different univariate time series methods with the purpose of providing future predictions of unemployment rate. In order to do that, several forecasting models (seasonal model autoregressive integrated moving average (SARIMA), self-exciting threshold autoregressive (SETAR), Holt–Winters, ETS (error, trend, seasonal), and NNAR (neural network autoregression)) have been applied, and their forecast performances have been evaluated on both the in-sample data covering the period January 2000–December 2017 used for the model identification and estimation and the out-of-sample data covering the last three years, 2018–2020. The forecast of unemployment rate relies on the next two years, 2021–2022. Based on the in-sample forecast assessment of different methods, the forecast measures root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) suggested that the multiplicative Holt–Winters model outperforms the other models. For the out-of-sample forecasting performance of models, RMSE and MAE values revealed that the NNAR model has better forecasting performance, while according to MAPE, the SARIMA model registers higher forecast accuracy. The empirical results of the Diebold–Mariano test at one forecast horizon for out-of-sample methods revealed differences in the forecasting performance between SARIMA and NNAR, of which the best model of modeling and forecasting unemployment rate was considered to be the NNAR model.

scholarly journals EXTREME RAINFALL FORECASTING MODEL BASED ON DESCRIPTIVE INDICES

2019 ◽  
Vol 14 (Number 1) ◽  
pp. 28-42
Author(s):  
Yasir Hilal Hadi ◽  
Ku Ruhana Ku-Mahamud ◽  
Wan Hussain Wan Ishak
Keyword(s):  
Mean Absolute Error ◽  
Back Propagation ◽  
Extreme Rainfall ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Forecasting Model ◽  
Rainfall Forecasting ◽  
Human Environment ◽  
Model Based ◽  
Rainfall Patterns

Extreme rainfall is one of the disastrous events that occurred due to massive rainfall overcometime beyond the regularrainfall rate. The catastrophic effects of extreme rainfall on human, environment, and economy are enormous as most of the events are unpredictable. Modelling the extreme rainfall patterns is a challenge since the extreme rainfall patterns are infrequent.In this study, a model based on descriptive indices to forecast extreme rainfall is proposed. The indices that are calculated every monthare used to develop a Back Propagation Neural Network model in forecasting extreme rainfall. Experiments were conducted using different combinations of indices and results were compared with actual data based on mean absolute error. The results showed that the combination of six indices achieved the best performance,and this proved that indices couldbe used for forecasting extreme rainfall values.

Performance Evaluation of Road Surface Temperature Forecasts

2020 ◽  
Author(s):  
Tae J. Kwon ◽  
Liping Fu
Keyword(s):  
Performance Evaluation ◽  
Surface Temperature ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Time Of Day ◽  
Forecast Horizon ◽  
Statistical Measures ◽  
Factors Influencing ◽  
Forecasting Performance ◽  
Bodies Of Water

This paper describes a study focusing on performance evaluation of RWIS surface temperature forecasts. To identify the factors influencing the accuracy of forecasts, five research hypotheses regarding climate patterns, geographical attributes, seasonal variations, time of day, and forecast-horizon are proposed. Observations and forecasts data were provided by four North American provinces and were processed and stratified by station, hour, and month to quantify their effects. The findings based on two statistical measures; namely, mean absolute error and percent of acceptable forecasts suggested that the maritime climate group had the highest correspondence while the mixed climate group had the lowest. The geographical attributes showed that the forecasting performance of coastal maritime regions was found to have a negative correlation to the distance from nearby large bodies of water. Forecasts for the daytime were found to be less accurate than for the nighttime and deteriorate quickly as the forecasting horizon increased.

scholarly journals A unified machine learning approach to time series forecasting applied to demand at emergency departments

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Michaela A.C. Vollmer ◽  
Ben Glampson ◽  
Thomas Mellan ◽  
Swapnil Mishra ◽  
Luca Mercuri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Departments ◽  
Linear Models ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Wide Range ◽  
The Future

Abstract Background There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide adequate quality of care while maintaining standards and productivity. Managing hospital demand effectively requires an adequate knowledge of the future rate of admission. We develop a novel predictive framework to understand the temporal dynamics of hospital demand. Methods We compare and combine state-of-the-art forecasting methods to predict hospital demand 1, 3 or 7 days into the future. In particular, our analysis compares machine learning algorithms to more traditional linear models as measured in a mean absolute error (MAE) and we consider two different hyperparameter tuning methods, enabling a faster deployment of our models without compromising performance. We believe our framework can readily be used to forecast a wide range of policy relevant indicators. Results We find that linear models often outperform machine learning methods and that the quality of our predictions for any of the forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. Our approach is able to predict attendances at these emergency departments one day in advance up to a mean absolute error of ±14 and ±10 patients corresponding to a mean absolute percentage error of 6.8% and 8.6% respectively. Conclusions Simple linear methods like generalized linear models are often better or at least as good as ensemble learning methods like the gradient boosting or random forest algorithm. However, though sophisticated machine learning methods are not necessarily better than linear models, they improve the diversity of model predictions so that stacked predictions can be more robust than any single model including the best performing one.

Contactless Core-Temperature Monitoring by Infrared Thermal Sensor using Mean Absolute Error Analysis

2020 ◽  
Vol 15 ◽  
Author(s):  
Fahad Layth Malallah ◽  
Baraa T. Shareef ◽  
Mustafah Ghanem Saeed ◽  
Khaled N. Yasen
Keyword(s):  
Body Temperature ◽  
Embedded System ◽  
Core Temperature ◽  
Integrated Circuit ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Thermal Sensor ◽  
Human Face ◽  
Body Contact ◽  
Arduino Microcontroller

Aims: Normally, the temperature increase of individuals leads to the possibility of getting a type of disease, which might be risky to other people such as coronavirus. Traditional techniques for tracking core-temperature require body contact either by oral, rectum, axillary, or tympanic, which are unfortunately considered intrusive in nature as well as causes of contagion. Therefore, sensing human core-temperature non-intrusively and remotely is the objective of this research. Background: Nowadays, increasing level of medical sectors is a necessary targets for the research operations, especially with the development of the integrated circuit, sensors and cameras that made the normal life easier. Methods: The solution is by proposing an embedded system consisting of the Arduino microcontroller, which is trained with a model of Mean Absolute Error (MAE) analysis for predicting Contactless Core-Temperature (CCT), which is the real body temperature. Results: The Arduino is connected to an Infrared-Thermal sensor named MLX90614 as input signal, and connected to the LCD to display the CCT. To evaluate the proposed system, experiments are conducted by participating 31-subject sensing contactless temperature from the three face sub-regions: forehead, nose, and cheek. Conclusion: Experimental results approved that CCT can be measured remotely depending on the human face, in which the forehead region is better to be dependent, rather than nose and cheek regions for CCT measurement due to the smallest

scholarly journals Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2670
Author(s):  
Thomas Quirin ◽  
Corentin Féry ◽  
Dorian Vogel ◽  
Céline Vergne ◽  
Mathieu Sarracanie ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Mean Absolute Error ◽  
Tracking System ◽  
Three Dimensional ◽  
Absolute Error ◽  
Magnetic Tracking ◽  
Magnetic Camera ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Brain

This paper presents a tracking system using magnetometers, possibly integrable in a deep brain stimulation (DBS) electrode. DBS is a treatment for movement disorders where the position of the implant is of prime importance. Positioning challenges during the surgery could be addressed thanks to a magnetic tracking. The system proposed in this paper, complementary to existing procedures, has been designed to bridge preoperative clinical imaging with DBS surgery, allowing the surgeon to increase his/her control on the implantation trajectory. Here the magnetic source required for tracking consists of three coils, and is experimentally mapped. This mapping has been performed with an in-house three-dimensional magnetic camera. The system demonstrates how magnetometers integrated directly at the tip of a DBS electrode, might improve treatment by monitoring the position during and after the surgery. The three-dimensional operation without line of sight has been demonstrated using a reference obtained with magnetic resonance imaging (MRI) of a simplified brain model. We observed experimentally a mean absolute error of 1.35 mm and an Euclidean error of 3.07 mm. Several areas of improvement to target errors below 1 mm are also discussed.

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