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 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.

scholarly journals ACCRUED FORECASTING ON TOURIST’S ARRIVAL IN BANGLADESH FOR SUSTAINABLE DEVELOPMENT

2021 ◽  
Vol 36 (2spl) ◽  
pp. 708-714
Author(s):  
Sayed Mohibul HOSSEN ◽  
◽  
Mohd Tahir ISMAIL ◽  
Mosab I. TABASH ◽  
Ahmed ABOUSAMAK ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Mean Squared Error ◽  
Moving Average ◽  
Critical Role ◽  
Absolute Error ◽  
Tourism Industry ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Autoregressive Integrated Moving Average ◽  
Private And Public

Forecasting of potential tourists’ appearance could assume a critical role in the tourism industry, arranging at all levels in both the private and public sectors. In this study our aim to build an econometric model to forecast worldwide visitor streams to Bangladesh. For this purpose, the present investigation focuses on univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling. Model choice criteria were Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (RMSE). As per descriptive statistics, the mean appearances were 207012 and will be 656522 (application) every year. Mean Absolute Deviation and Mean Squared Deviation likewise concurred with MAPE, MAE, and MSE. The result reveals that for sustainable development the SARIMA model is the reasonable model for forecasting universal visitor appearances in Bangladesh.

scholarly journals A study of univariate forecasting methods for crude oil price

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mei-Ling Cheng ◽  
Ching-Wu Chu ◽  
Hsiu-Li Hsu
Keyword(s):  
Regression Model ◽  
Crude Oil ◽  
Mean Squared Error ◽  
Moving Average ◽  
Model Performance ◽  
Absolute Error ◽  
Oil Price ◽  
Crude Oil Price ◽  
Content Type ◽  
Forecasting Methods

PurposeThis paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.Design/methodology/approachSix different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.FindingsThe authors found that the grey forecast is a reliable forecasting method for crude oil prices.Originality/valueThe contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

scholarly journals Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea

2020 ◽  
Vol 17 (10) ◽  
pp. 3520 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Ahmed A. Ewees ◽  
Hong Fan ◽  
Laith Abualigah ◽  
Mohamed Abd Elaziz
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Inference System ◽  
Infected People ◽  
Marine Predators ◽  
Forecasting Performance ◽  
The Usa

The current pandemic of the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination( R 2 ). For instance, according to the results of the testing set, the R 2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively.

scholarly journals Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

Forecasting ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 211-229
Author(s):  
Ulrich Gunter ◽  
Irem Önder ◽  
Egon Smeral
Keyword(s):  
Moving Average ◽  
Tourism Demand ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Forecast Combinations ◽  
The Stability ◽  
Forecasting Errors ◽  
Macroeconomic Environment

This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively (i.e., based on expanding estimation windows) for eight quarterly periods spanning two years in order to check the stability of the outcomes during a changing macroeconomic environment. The study sample includes Eurostat data from January 2005 until August 2017, and out of sample forecasts were calculated for the last two years for three and six months ahead. The analysis of the out-of-sample forecasts for arrivals and overnights showed that forecast combinations taking the historical forecasting performance of individual approaches such as Autoregressive Integrated Moving Average (ARIMA) models, REGARIMA models with different trend variables, and Error Trend Seasonal (ETS) models into account deliver the best results.

MULTIVARIATE NONLINEAR FORECASTING Using Financial Information to Forecast the Real Sector

1998 ◽  
Vol 2 (3) ◽  
pp. 369-382 ◽  
Author(s):  
Ted Jaditz ◽  
Leigh A. Riddick ◽  
Chera L. Sayers
Keyword(s):  
Nearest Neighbor ◽  
Mean Squared Error ◽  
Forecast Accuracy ◽  
Financial Information ◽  
Real Sector ◽  
The Real ◽  
Financial Variables ◽  
Out Of Sample ◽  
State Of The Economy ◽  
Nonlinear Forecasting

Previous work shows that financial series contain important information on the current state of the economy and expectations for the future. Further, numerous papers find links between the financial sectors and the real sectors of the economy. We add to those findings by exploring whether financial variables help to forecast the growth rate of industrial production. We evaluate linear and nonlinear forecasting methods using out-of-sample forecasting performance. We compare autoregressive models, error-correcting models, and multivariate nearest-neighbor regression models, and we explore the use of optimally combined forecasts. We find that no single forecasting technique appears to outperform any other method, and the evidence for persistent nonlinear patterns is weak. However, although nonparametric methods do not offer significant improvements in forecast accuracy by themselves, more accurate forecasts are obtained when the nonlinear forecasts are optimally combined. Our results indicate that financial information can statistically improve the forecasts of the real sector in these combined models, but the magnitude of the improvement in root-mean-squared error is small.

scholarly journals COVID-19 Infection Forecasting based on Deep Learning in Iran

2020 ◽  
Author(s):  
Mehdi Azarafza ◽  
Mohammad Azarafza ◽  
Jafar Tanha
Keyword(s):  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
National Level ◽  
Absolute Error ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
Error Metrics ◽  
Squared Error ◽  
Better Than

Since December 2019 coronavirus disease (COVID-19) is outbreak from China and infected more than 4,666,000 people and caused thousands of deaths. Unfortunately, the infection numbers and deaths are still increasing rapidly which has put the world on the catastrophic abyss edge. Application of artificial intelligence and spatiotemporal distribution techniques can play a key role to infection forecasting in national and province levels in many countries. As methodology, the presented study employs long short-term memory-based deep for time series forecasting, the confirmed cases in both national and province levels, in Iran. The data were collected from February 19, to March 22, 2020 in provincial level and from February 19, to May 13, 2020 in national level by nationally recognised sources. For justification, we use the recurrent neural network, seasonal autoregressive integrated moving average, Holt winter's exponential smoothing, and moving averages approaches. Furthermore, the mean absolute error, mean squared error, and mean absolute percentage error metrics are used as evaluation factors with associate the trend analysis. The results of our experiments show that the LSTM model is performed better than the other methods on the collected COVID-19 dataset in Iran

scholarly journals Forecasting Natural Rubber Price in Malaysia by 2030

2021 ◽  
Vol 6 (9) ◽  
pp. 382-390
Author(s):  
Nor Farah Hanim Binti Mohamad Norizan ◽  
Zahayu Binti Md Yusof
Keyword(s):  
Measurement Error ◽  
Natural Rubber ◽  
Mean Squared Error ◽  
Moving Average ◽  
Time Lag ◽  
Absolute Error ◽  
Percentage Error ◽  
Economic Sectors ◽  
Future Data ◽  
Double Exponential Smoothing

Natural rubber (NR) has recently become one of Malaysia's most important economic sectors. Despite, the price of Standard Malaysia Rubber 20 changes frequently. That is why it is important to develop a NR price forecasting model. Because there was a significant time lag between making output decisions and the actual output of the commodity in the market. The aim of this study is to determine the time series pattern for natural rubber price in Malaysia within 1995 until 2020 and to forecast the natural rubber price in Malaysia for 10 years ahead. The data used is from year 1995 until 2020 that were obtained from Malaysian Rubber Board (MRB). This study also used univariate forecasting like Naïve with Trend, Double Exponential Smoothing, Holt’s Winter and Autoregressive Integrated Moving Average (ARIMA). Then, the measurement error is used to determine the best method to forecast the future data. The measurement error that used in this study are Mean Absolute Error, Mean Squared Error, Root Mean Square Error, Mean Absolute Percentage Error and The Theil Inequality Coefficient. Result: The natural rubber price in Malaysia showed a trend pattern. Then, ARIMA is used to determine the forecast of natural rubber price for next 10 years since it has the lowest measurement error. Conclusion: There are volatility in the price of natural rubber in Malaysia over the next 10 years.

scholarly journals Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models

2020 ◽  
Author(s):  
Leila MOFTAKHAR ◽  
Mozhgan SEIF ◽  
Marziyeh Sadat SAFE
Keyword(s):  
Model Comparison ◽  
Mean Squared Error ◽  
Moving Average ◽  
Absolute Error ◽  
Decision Makers ◽  
Squared Error ◽  
Number Of Patients ◽  
Auto Regressive ◽  
Test Sets ◽  
Open Datasets

Background: The outbreak of COVID-19 is rapidly spreading around the world and became a pandemic disease. For help to better planning of interventions, this study was conducted to forecast the number of daily new infected cases with COVID-19 for next thirty days in Iran. Methods: The information of observed Iranian new cases from 19th Feb to 30th Mar 2020 was used to predict the number of patients until 29th Apr. Artificial Neural Networks (ANN) and Auto-Regressive Integrated Moving Average (ARIMA) models were applied for prediction. The data was prepared from daily reports of Iran Ministry of Health and open datasets provided by the JOHN Hopkins. To compare models, dataset was separated into train and test sets. Mean Squared Error (MSE) and Mean Absolute Error (MAE) was the comparison criteria. Results: Both algorithms forecasted an exponential increase in number of newly infected patients. If the spreading pattern continues the same as before, the number of daily new cases would be 7872 and 9558 by 29th Apr, respectively by ANN and ARIMA. While Model comparison confirmed that ARIMA prediction was more accurate than ANN. Conclusion: COVID-19 is contagious disease, and has infected many people in Iran. Our results are an alarm for health policy planners and decision-makers, to make timely decisions, control the disease and provide the equipment needed.

FUZZY TIME SERIES AND SARIMA MODEL FOR FORECASTING TOURIST ARRIVALS TO BALI

2012 ◽  
Vol 57 (1) ◽  
Author(s):  
Maria Elena ◽  
Muhamad Hisyam Lee ◽  
Suhartono H. ◽  
Hossein I. ◽  
Nur Haizum Abd Rahman ◽  
...  
Keyword(s):  
Time Series ◽  
Classical Method ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Fuzzy Time Series ◽  
Strategic Decisions ◽  
Clear Cut ◽  
Forecasting Method ◽  
Forecasting Performance ◽  
Future Events

Forecasting is very important in many types of organizations since predictions of future events must be incorporated into the decision–making process. In the case of tourism demand, better forecast would help directors and investors make operational, tactical, and strategic decisions. Generally, in time series we can divide forecasting method into classical method and modern methods. Although recent studies show that the newer and more advanced forecasting techniques tend to result in improved forecast accuracy under certain circumstances, no clear–cut evidence shows that any one model can consistently outperform other models in the forecasting competition [1]. In this study, the forecasting performance between Box–Jenkins approaches of seasonal autoregressive integrated moving average (SARIMA) and four models of fuzzy time series has been compared by using MAPE, MAD and RMSE as the forecast measures of accuracy. The empirical results show that Chen's fuzzy time series model outperforms the SARIMA and the other fuzzy time series models.

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