Hybrid Neural Models For Rice Yields Times Forecasting

In this paper, time series prediction is considered as a problem of missing value. A model for the determination of the missing time series value is presented. The hybrid model integrating autoregressive intergrated moving average (ARIMA) and artificial neural network (ANN) model is developed to solve this problem. The developed models attempts to incorporate the linear characteristics of an ARIMA model and nonlinear patterns of ANN to create a hybrid model. In this study, time series modeling of rice yield data in Muda Irrigation area. Malaysia from 1995 to 2003 are considered. Experimental results with rice yields data sets indicate that the hybrid model improve the forecasting performance by either of the models used separately. Key words: ARIMA; Box and Jenkins; neural networks; rice yields; hybrid ANN model

The Forecasting of the Elevator Traffic Flow Time Series Based on ARIMA and GP

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.588-589.1466 ◽

2012 ◽

Vol 588-589 ◽

pp. 1466-1471 ◽

Cited By ~ 1

Author(s):

Jun Fang Li ◽

Qun Zong

Keyword(s):

Time Series ◽

Traffic Flow ◽

Linear Models ◽

Moving Average ◽

Arima Model ◽

Flow Time ◽

Forecasting Model ◽

Hybrid Forecasting ◽

As one of the conventional statistical methods, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but it is difficult to explain the meaning of the hidden layers of ANN and it does not produce a mathematical equation. In this study, by combining ARIMA with genetic programming (GP), a hybrid forecasting model will be used for elevator traffic flow time series which can improve the accuracy both the GP and the ARIMA forecasting models separately. At last, simulations are adopted to demonstrate the advantages of the proposed ARIMA-GP forecasting model.

A New Strategy of Hybrid Models using ARIMA, ANN, and DWT in Time Series Modelling

Journal of Statistics Advances in Theory and Applications ◽

10.18642/jsata_7100122182 ◽

2021 ◽

Vol 25 (1) ◽

pp. 27-50

Author(s):

Tsung-Lin Li ◽

◽

Chen-An Tsai ◽

Keyword(s):

Time Series ◽

Moving Average ◽

Arima Model ◽

Real Data ◽

Hybrid Models ◽

Discrete Wavelet ◽

Data Sets ◽

Model Combining ◽

Time Series Modelling ◽

Significant Difference

Time series forecasting is a challenging task of interest in many disciplines. A variety of techniques have been developed to deal with the problem through a combination of different disciplines. Although various researches have proved successful for hybrid models, none of them carried out the comparisons with solid statistical test. This paper proposes a new stepwise model determination method for artificial neural network (ANN) and a novel hybrid model combining autoregressive integrated moving average (ARIMA) model, ANN and discrete wavelet transformation (DWT). Simulation studies are conducted to compare the performance of different models, including ARIMA, ANN, ARIMA-ANN, DWT-ARIMA-ANN and the proposed method, ARIMA-DWT-ANN. Also, two real data sets, Lynx data and cabbage data, are used to demonstrate the applications. Our proposed method, ARIMA-DWT-ANN, outperforms other methods in both simulated datasets and Lynx data, while ANN shows a better performance in the cabbage data. We conducted a two-way ANOVA test to compare the performances of methods. The results showed a significant difference between methods. As a brief conclusion, it is suggested to try on ANN and ARIMA-DWT-ANN due to their robustness and high accuracy. Since the performance of hybrid models may vary across data sets based on their ARIMA alike or ANN alike natures, they should all be considered when encountering a new data to reach an optimal performance.

Real-Time Forecasting of COVID-19 prevalence in India using ARIMA Model

Regular issue - International Journal of Management and Humanities ◽

10.35940/ijmh.j0973.0641020 ◽

2020 ◽

Vol 4 (10) ◽

pp. 78-83

Keyword(s):

Time Series ◽

Virus Disease ◽

Moving Average ◽

Time Series Prediction ◽

Arima Model ◽

Daily Data ◽

The World ◽

Increasing Trend ◽

Short Term Prediction

Corona virus disease (COVID -19) has changed the world completely due to unavailability of its exact treatment. It has affected 215 countries in the world in which India is no exception where COVID patients are increasing exponentially since 15th of Feb. The objective of paper is to develop a model which can predict daily new cases in India. The autoregressive integrated moving average (ARIMA) models have been used for time series prediction. The daily data of new COVID-19 cases act as an exogenous variable in this framework. The daily data cover the sample period of 15th February, 2020 to 24th May, 2020. The time variable under study is a non-stationary series as 𝒚𝒕 is regressed with 𝒚𝒕−𝟏 and the coefficient is 1. The time series have clearly increasing trend. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction. In PACF graph. Lag 1 and Lag 13 is significant. Regressed values implies Lag 1 and Lag 13 is significant in predicting the current values. The model predicted maximum COVID-19 cases in India at around 8000 during 5thJune to 20th June period. As per the model, the number of new cases shall start decreasing after 20th June in India only. The results will help governments to make necessary arrangements as per the estimated cases. The limitation of this model is that it is unable to predict jerks on either lower or upper side of daily new cases. So, in case of jerks re-estimation will be required.

River flow time series using least squares support vector machines

Hydrology and Earth System Sciences ◽

10.5194/hess-15-1835-2011 ◽

2011 ◽

Vol 15 (6) ◽

pp. 1835-1852 ◽

Cited By ~ 52

Author(s):

R. Samsudin ◽

P. Saad ◽

A. Shabri

Keyword(s):

Time Series ◽

Least Squares ◽

Hybrid Model ◽

River Flow ◽

Moving Average ◽

Peninsular Malaysia ◽

Support Vector ◽

Group Method ◽

Input Variables ◽

Abstract. This paper proposes a novel hybrid forecasting model known as GLSSVM, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM). The GMDH is used to determine the useful input variables which work as the time series forecasting for the LSSVM model. Monthly river flow data from two stations, the Selangor and Bernam rivers in Selangor state of Peninsular Malaysia were taken into consideration in the development of this hybrid model. The performance of this model was compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA), GMDH and LSSVM models using the long term observations of monthly river flow discharge. The root mean square error (RMSE) and coefficient of correlation (R) are used to evaluate the models' performances. In both cases, the new hybrid model has been found to provide more accurate flow forecasts compared to the other models. The results of the comparison indicate that the new hybrid model is a useful tool and a promising new method for river flow forecasting.

Prediction of Gold and Silver Prices in an Emerging Economy: Comparative Analysis of Linear, Nonlinear, Hybrid, and Ensemble Models

The Journal of Prediction Markets ◽

10.5750/jpm.v12i3.1669 ◽

2019 ◽

Vol 12 (3) ◽

pp. 63-78

Author(s):

Saurabh Kumar

Keyword(s):

Time Series Data ◽

Moving Average ◽

Arima Model ◽

Emerging Economy ◽

Series Data ◽

Ensemble Model ◽

Ann Model ◽

Ensemble Models ◽

This study compares the accuracy of different forecasting techniques for gold and silver returns in a leading emerging economy. The study employs four forecasting models: autoregressive integrated moving average (ARIMA), artificial neural network (ANN), hybrid, and ensemble models. The study takes data of more than 7 years and forecasting is carried out for different forecast horizons varying from 1- to 20-steps ahead. The results reveal that ARIMA model is the best model to predict the gold returns, whereas, the ANN model along with the ensemble model are the best to predict the silver returns. The results also indicate that there exists nonlinear patterns in the time-series data of gold and silver returns. The study has significant implications for investors, academia, and policymakers.

Using Hybrid ARIMAX-ANN Model for Simulating Rainfall - Runoff - Sediment Process Case Study

International Journal of Applied Metaheuristic Computing ◽

10.4018/jamc.2013040104 ◽

2013 ◽

Vol 4 (2) ◽

pp. 44-60 ◽

Cited By ~ 2

Author(s):

Vahid Nourani ◽

Samira Roumianfar ◽

Elnaz Sharghi

Keyword(s):

Time Series ◽

Linear Models ◽

Sediment Load ◽

Moving Average ◽

Linear Component ◽

Rainfall Runoff ◽

Ann Model ◽

Non Linear ◽

Runoff Sediment ◽

The need for accurate modeling of rainfall-runoff-sediment processes has grown rapidly in the past decades. This study investigates the efficiency of black-box models including Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average with eXogenous input (ARIMAX) models for forecasting the rainfall-runoff-sediment process. According to the complex behavior of the rainfall-runoff-sediment time series, they include both linear and nonlinear components; therefore, employing a hybrid model which combines the advantages of both linear and non-linear models improves the accuracy of prediction. In this paper, a hybrid of ARIMAX-ANN model is applied to rainfall-runoff-sediment modeling of a watershed. At the first step of the hybrid modeling, the ARIMAX method is applied to forecast the linear component of the rainfall-runoff process and then in the second step, an ANN model is used to find the non-linear relationship among the residuals of the fitted linear ARIMAX model. Finally, total effective time series of runoff, obtained by the hybrid ARIMAX-ANN model are imposed as input to the proposed ANN model for prediction daily suspended sediment load of the watershed. The proposed model is more appropriate, as it uses the semi-linear relation for prediction of sediment load.

A hybrid of artificial fneural network, exponential smoothing, and ARIMA models for COVID-19 time series forecasting

Model Assisted Statistics and Applications ◽

10.3233/mas-210512 ◽

2021 ◽

Vol 16 (1) ◽

pp. 25-35

Author(s):

Samir K. Safi ◽

Olajide Idris Sanusi

Keyword(s):

Time Series ◽

Moving Average ◽

Arima Model ◽

Absolute Error ◽

Time Series Forecasting ◽

Arima Models ◽

Artificial Neural Network Ann ◽

Novel Coronavirus ◽

Two Stages ◽

Second Wave

The Auto Regressive Integrated Moving Average (ARIMA) model seems not to easily capture the nonlinear patterns exhibited by the 2019 novel coronavirus (COVID-19) in terms of daily confirmed cases. As a result, Artificial Neural Network (ANN) and Error, Trend, and Seasonality (ETS) modeling have been successfully applied to resolve problems with nonlinear estimation. Our research suggests that it would be ideal to use a single model of ETS or ARIMA for COVID-19 time series forecasting rather than a complicated Hybrid model that combines several models. We compare the forecasting performance of these models using real, worldwide, daily COVID-19 data for the period between January 22, 2020 till June 19, and June 20 till January 2, 2021 which marks two stages, each stage indicating the first and the second wave respectively. We discuss various forecasting approaches and the criteria for choosing the best forecasting technique. The best forecasting model selected was compared using the forecasting assessment criterion known as Mean Absolute Error (MAE). The empirical results show that the ETS and ARIMA models outperform the ANN and Hybrid models. The main finding from the ETS and ARIMA models analysis indicate that the magnitude of the increase in total confirmed cases over time is declining and the percentage change in the death rate is also on the decline. Our results shows that the chosen forecaste models are consistent during the first and second wave of of the pandemic. These forecasts are encouraging as the world struggles to contain the spread of COVID-19. This may be the result of the social distancing measures mandated by governments worldwide.

Adaptive Online Learning for Time Series Prediction

10.20944/preprints202104.0601.v1 ◽

2021 ◽

Author(s):

Weijia Shao ◽

Lukas Friedemann Radke ◽

Fikret Sivrikaya ◽

Sahin Albayrak

Keyword(s):

Time Series ◽

Online Learning ◽

Time Series Data ◽

Moving Average ◽

Time Series Prediction ◽

Arima Model ◽

Series Data ◽

Real World Datasets ◽

Regret Bound ◽

Adaptive Online Learning

We study the problem of predicting time series data using the autoregressive integrated moving average (ARIMA) model in an online manner. Existing algorithms require model selection, which is time consuming and inapt for the setting of online learning. Using adaptive online learning techniques, we develop algorithms for fitting ARIMA models with fewest possible hyperparameters. We analyse the regret bound of the proposed algorithms and examine their performance using experiments on both synthetic and real world datasets

Optimization of ARIMA Forecasting Model using Firefly Algorithm

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.37666 ◽

2019 ◽

Vol 13 (2) ◽

pp. 127 ◽

Cited By ~ 1

Author(s):

Ilham Unggara ◽

Aina Musdholifah ◽

Anny Kartika Sari

Keyword(s):

Time Series ◽

Firefly Algorithm ◽

Moving Average ◽

Time Series Prediction ◽

Arima Model ◽

Information Criterion ◽

Forecasting Model ◽

Optimal Model ◽

Foreign Tourist

Time series prediction aims to control or recognize the behavior of the system based on the data in a certain period of time. One of the most widely used method in time series prediction is ARIMA (Autoregressive Integrated Moving Average). However, ARIMA has a weakness in determining the optimal model. firefly algorithm is used to optimize ARIMA model (p, d, q). by finding the smallest AIC (Akaike Information Criterion) value in determining the best ARIMA model. The data used in the study are daily stock data JCI period January 2013 until August 2016 and data of foreign tourist visits to Indonesia period January 1988 to November 2017.Based on testing, for JCI data, obtained predicted results with Box-Jenkins ARIMA model produces RMSE 49.72, whereas the prediction with the ARIMA Optimization model yielded RMSE 49.48. For the data of Foreign Tourist Visits, the predicted results with the Box-Jenkins ARIMA model resulted in RMSE 46088.9, whereas the predicted results with ARIMA optimization resulted in RMSE 44678.4. From these results it can be concluded that the optimization of ARIMA model with Firefly Algorithm produces better forecasting model than ARIMA model without Optimization.

Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study

BMJ Open ◽

10.1136/bmjopen-2018-025773 ◽

2019 ◽

Vol 9 (6) ◽

pp. e025773 ◽

Cited By ~ 7

Author(s):

Ya-wen Wang ◽

Zhong-zhou Shen ◽

Yu Jiang

Keyword(s):

Time Series ◽

Hybrid Model ◽

Moving Average ◽

Arima Model ◽

Haemorrhagic Fever ◽

Time Series Study ◽

Series Study ◽

Monthly Incidence ◽

Better Than

ObjectivesHaemorrhagic fever with renal syndrome (HFRS) is a serious threat to public health in China, accounting for almost 90% cases reported globally. Infectious disease prediction may help in disease prevention despite some uncontrollable influence factors. This study conducted a comparison between a hybrid model and two single models in forecasting the monthly incidence of HFRS in China.DesignTime-series study.SettingThe People’s Republic of China.MethodsAutoregressive integrated moving average (ARIMA) model, generalised regression neural network (GRNN) model and hybrid ARIMA-GRNN model were constructed by R V.3.4.3 software. The monthly reported incidence of HFRS from January 2011 to May 2018 were adopted to evaluate models’ performance. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were adopted to evaluate these models’ effectiveness. Spatial stratified heterogeneity of the time series was tested by month and another GRNN model was built with a new series.ResultsThe monthly incidence of HFRS in the past several years showed a slight downtrend and obvious seasonal variation. A total of four plausible ARIMA models were built and ARIMA(2,1,1) (2,1,1)12model was selected as the optimal model in HFRS fitting. The smooth factors of the basic GRNN model and the hybrid model were 0.027 and 0.043, respectively. The single ARIMA model was the best in fitting part (MAPE=9.1154, MAE=89.0302, RMSE=138.8356) while the hybrid model was the best in prediction (MAPE=17.8335, MAE=152.3013, RMSE=196.4682). GRNN model was revised by building model with new series and the forecasting performance of revised model (MAPE=17.6095, MAE=163.8000, RMSE=169.4751) was better than original GRNN model (MAPE=19.2029, MAE=177.0356, RMSE=202.1684).ConclusionsThe hybrid ARIMA-GRNN model was better than single ARIMA and basic GRNN model in forecasting monthly incidence of HFRS in China. It could be considered as a decision-making tool in HFRS prevention and control.