scholarly journals STREAMFLOW FORECASTING IN BUKIT MERAH WATERSHED BY USING ARIMA AND ANN

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Muhammad Reza ◽  
Sobri Harun ◽  
Muhammad Askari
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Linear Time ◽  
Arima Model ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Ann Model ◽  
Streamflow Forecasting ◽  
Monthly Streamflow ◽  
Non Linear

This paper presents the application of linear and non-linear time series modeling approaches for simulating and forecasting streamflow at three stations located in three different rivers namely Kurau River, Ara River and Krian River of Bukit Merah watershed of Malaysia. The performance of linear autoregressive integrated moving average (ARIMA) model and non-linear artificial neural networks (ANN) model in forecasting the monthly streamflow of Malaysian river basins has been evaluated based on mean absolute percentage error (MAPE), root mean squared error (RMSE) and coefficient of determination (R2). The results show that both ARIMA and ANN methods are suitable for streamflow forecasting. However, ANN is better than ARIMA in dealing with short-memory streamflow data. In addition, ANN method is more flexible to use against the inconsistent data.Keywords: time series, streamflow forecasting, ARIMA, ANN, Bukit Merah

scholarly journals Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study

2021 ◽  
Author(s):  
Nabeel H. Al-Saati ◽  
Isam I. Omran ◽  
Alaa Ali Salman ◽  
Zainab Al-Saati ◽  
Khalid S. Hashim
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Moving Average ◽  
Stationary Time Series ◽  
Coefficient Of Determination ◽  
Neural Network Models ◽  
Monthly Streamflow ◽  
Artificial Neural

Abstract Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins models combine the autoregressive and moving average models to a stationary time series after the appropriate transformation, while the nonlinear autoregressive (N.A.R.) or the autoregressive neural network (ARNN) models are of the kind of multi-layer perceptron (M.L.P.), which compose an input layer, hidden layer and an output layer. Monthly streamflow at the downstream of the Euphrates River (Hindiya Barrage) /Iraq for the period January 2000 to December 2019 was modeled utilizing ARIMA and N.A.R. time series models. The predicted Box-Jenkins model was ARIMA (1,1,0) (0,1,1), while the predicted artificial neural network (N.A.R.) model was (M.L.P. 1-3-1). The results of the study indicate that the traditional Box-Jenkins model was more accurate than the N.A.R. model in modeling the monthly streamflow of the studied case. Performing a one-step-ahead forecast during the year 2019, the forecast accuracy between the forecasted and recorded monthly streamflow for both models was as follows: the Box-Jenkins model gave root mean squared error (RMSE = 48.7) and the coefficient of determination R2 = 0.801), while the (NAR) model gave (RMSE = 93.4) and R2 = 0.269). Future projection of the monthly stream flow through the year 2025, utilizing the Box-Jenkins model, indicated the existence of long-term periodicity.

scholarly journals Comparison of daily rainfall forecasting using multilayer perceptron neural network model

2020 ◽  
Vol 9 (3) ◽  
pp. 456
Author(s):  
Mazwin Arleena Masngut ◽  
Shuhaida Ismail ◽  
Aida Mustapha ◽  
Suhaila Mohd Yasin
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Forecast Error ◽  
Arima Model ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Rainfall Forecasting ◽  
Agriculture Sector

Rainfall is important in predicting weather forecast particularly to the agriculture sector and also in environment which gives great contribution towards the economy of the nation. Thus, it is important for the hydrologists to forecast daily rainfall in order to help the other people in the agriculture sector to proceed with their harvesting schedules accordingly and to make sure the results of their crops would be satisfying. This study is set to forecast the daily rainfall future value using ARIMA model and Artificial Neural Network (ANN) model. Both method is evaluated by using Mean Absolute Error (MAE), Mean Forecast Error (MFE), Root Mean Squared Error (RMSE) and coefficient of determination (R ). The results showed that ANN model has outperformed results than ARIMA model. The results also showed ANN has under-forecast the daily rainfall data by 2.21% compare to ARIMA with over-forecast of -3.34%. From this study, it shows that the ANN (6,4,1) model produces better results of MAE (8.4208), MFE (2.2188), RMSE (34.6740) and R (0.9432) compared to ARIMA model. This has proved that ANN model has outperformed ARIMA model in predicting daily rainfall values.

scholarly journals Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248597
Author(s):  
Guo-hua Ye ◽  
Mirxat Alim ◽  
Peng Guan ◽  
De-sheng Huang ◽  
Bao-sen Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Arima Model ◽  
Mainland China ◽  
Hemorrhagic Fever ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Performance ◽  
The Future

Objective Hemorrhagic fever with renal syndrome (HFRS), one of the main public health concerns in mainland China, is a group of clinically similar diseases caused by hantaviruses. Statistical approaches have always been leveraged to forecast the future incidence rates of certain infectious diseases to effectively control their prevalence and outbreak potential. Compared to the use of one base model, model stacking can often produce better forecasting results. In this study, we fitted the monthly reported cases of HFRS in mainland China with a model stacking approach and compared its forecasting performance with those of five base models. Method We fitted the monthly reported cases of HFRS ranging from January 2004 to June 2019 in mainland China with an autoregressive integrated moving average (ARIMA) model; the Holt-Winter (HW) method, seasonal decomposition of the time series by LOESS (STL); a neural network autoregressive (NNAR) model; and an exponential smoothing state space model with a Box-Cox transformation; ARMA errors; and trend and seasonal components (TBATS), and we combined the forecasting results with the inverse rank approach. The forecasting performance was estimated based on several accuracy criteria for model prediction, including the mean absolute percentage error (MAPE), root-mean-squared error (RMSE) and mean absolute error (MAE). Result There was a slight downward trend and obvious seasonal periodicity inherent in the time series data for HFRS in mainland China. The model stacking method was selected as the best approach with the best performance in terms of both fitting (RMSE 128.19, MAE 85.63, MAPE 8.18) and prediction (RMSE 151.86, MAE 118.28, MAPE 13.16). Conclusion The results showed that model stacking by using the optimal mean forecasting weight of the five abovementioned models achieved the best performance in terms of predicting HFRS one year into the future. This study has corroborated the conclusion that model stacking is an easy way to enhance prediction accuracy when modeling HFRS.

scholarly journals Monthly streamflow prediction using a hybrid stochastic-deterministic approach for parsimonious non-linear time series modeling

2020 ◽  
Vol 14 (1) ◽  
pp. 1351-1372
Author(s):  
Zhen Wang ◽  
Nasrin Fathollahzadeh Attar ◽  
Keivan Khalili ◽  
Javad Behmanesh ◽  
Shahab S. Band ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Time ◽  
Time Series Modeling ◽  
Deterministic Approach ◽  
Streamflow Prediction ◽  
Monthly Streamflow ◽  
Non Linear

Performance analysis of hot metal temperature prediction in a blast furnace and expert suggestion system proposal using neural, statistical and fuzzy models

2021 ◽  
Vol 118 (3) ◽  
pp. 321
Author(s):  
Erdoğan Bozkurt ◽  
İlhami M. Orak ◽  
Yasin Tunçkaya
Keyword(s):  
Blast Furnace ◽  
Mean Squared Error ◽  
Arima Model ◽  
Flame Temperature ◽  
Metal Temperature ◽  
Raw Material ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Chemical Compositions ◽  
Hot Metal

Blast Furnace (BF) production methodology is one of the most complex process of iron & steel plants as it is dependent on multi-variable process inputs and disturbances to be modelled properly. Due to expensive investment costs, it is critical to operate a BF by reducing operational expenses, increasing the performance of raw material and fuel consumptions to optimize overall furnace efficiency and stability, also to maximize the lifetime. The chemical compositions and temperature of hot metal are important indicators while evaluating the operation, therefore, if the future values of hot metal temperature can be predicted in advance instead of subsequent measuring, then the BF staff can take earlier counteractions on several operational parameters such as coke to ore ratio, distribution matrix, oxygen enrichment rate, blast moisture rate, permeability, flame temperature, cold blast temperature, cold blast flow and pulverized coal injection rate, etc. to control the furnace optimally. In this study, Artificial Neural Networks (ANN) model is proposed combined with NARX (Nonlinear autoregressive exogenous model) time series approach to track and predict furnace hot metal temperature by selecting the most suitable process inputs and past values of hot metal temperatures using the real data which is collected from the BF operated in Turkey during 2 months of operation. Various data mining techniques are applied due to requirements of charge cycling and operating speed of the furnace which secures novelty and effectiveness of this study comparing previous articles. Furthermore, a statistical tool, Autoregressive Integrated Moving Average (ARIMA) model, is also executed for comparison. ANN prediction results of 0.92, 8.59 and 0.41 are found very satisfactory comparing ARIMA (1,1,1) model outputs of 0.73, 97.4 and 9.32 for R2 (Coefficient of determination), RMSE (Root mean squared error) and MAPE (Mean absolute percentage error) respectively. Consequently, an expert suggestion system is proposed using fuzzy if-then rules with 5 × 5 probability matrix design using the last predicted HMT value and the average of the last 5 HMT values to decide furnace’s warming or cooling movements state in mid-term and maintain the operational actions interactively in advance.

scholarly journals FORECASTING GOLD PRICE CHANGES BY USING ADAPTIVE NETWORK FUZZY INFERENCE SYSTEM

2012 ◽  
Vol 13 (5) ◽  
pp. 994-1010 ◽  
Author(s):  
Abdolreza Yazdani-Chamzini ◽  
Siamak Haji Yakhchali ◽  
Diana Volungevičienė ◽  
Edmundas Kazimieras Zavadskas
Keyword(s):  
Fuzzy Inference System ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Arima Model ◽  
Gold Price ◽  
Performance Criteria ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Price Changes ◽  
Inference System

Developing a precise and accurate model of gold price is critical to assets management because of its unique features. In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) model have been used for modeling the gold price, and compared with the traditional statistical model of ARIMA (autoregressive integrated moving average). The three performance measures, the coefficient of determination (R 2), root mean squared error (RMSE), mean absolute error (MAE), are utilized to evaluate the performances of different models developed. The results show that the ANFIS model outperforms other models (i.e. ANN and ARIMA model), in terms of different performance criteria during the training and validation phases. Sensitivity analysis showed that the gold price changes are highly dependent upon the values of silver price and oil price.

scholarly journals Hybrid Time Series Models for Forecasting Maize Production in India

2021 ◽  
pp. 49-57
Author(s):  
Pramit Pandit ◽  
Bishvajit Bakshi ◽  
Varun Gangadhar
Keyword(s):  
Time Series ◽  
Model Building ◽  
Linear Models ◽  
Linear Time ◽  
Arima Model ◽  
Hybrid Models ◽  
Time Series Models ◽  
Real World Data ◽  
Maize Production ◽  
Non Linear

In spite of the immense success of different linear and non-linear time series models in their respective domains, real-world data are rarely pure linear or non-linear in nature. Hence, a hybrid modelling framework with the capability of handling both linear and non-linear patterns can substantially improve the forecasting accuracy. With this backdrop, an effort has been made in this investigation to evaluate the suitability of hybrid models in compassion to single linear or non-linear models for forecasting maize production in India. Data from 1949-50 to 2016-17 have been utilised for the model building purpose while retaining the data from 2017-18 to 2019-20 for the post-sample accuracy assessment. Outcomes emanated from this investigation clearly reveals that the ARIMA-NLSVR model has outperformed all other candidate models employed in this study. It is noteworthy to mention that both the hybrid models have performed better than their individual counterparts. The superior forecasting ability of both the non-linear models over the linear ARIMA model has also been evident.

Nonlinear Time Series Analysis with R

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Particle Physics ◽  
Linear Time ◽  
Nonlinear Time Series ◽  
Nonlinear Time Series Analysis ◽  
Series Analysis ◽  
Linear Behavior ◽  
Non Linear ◽  
Scientific Principles

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjects of science, such as mathematical topology, relativity or particle physics. For this reason, the tools of NLTS have been confined and utilized mostly in the fields of mathematics and physics. However, many natural phenomena investigated I many fields have been revealing deterministic non linear structures. In this book we aim at presenting the theory and the empirical of NLTS to a broader audience, to make this very powerful area of science available to many scientific areas. This book targets students and professionals in physics, engineering, biology, agriculture, economy and social sciences as a textbook in Nonlinear Time Series Analysis (NLTS) using the R computer language.

scholarly journals How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

2021 ◽  
Vol 149 ◽  
Author(s):  
Junwen Tao ◽  
Yue Ma ◽  
Xuefei Zhuang ◽  
Qiang Lv ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Variable Selection ◽  
Mean Squared Error ◽  
Moving Average ◽  
Dynamic Bayesian Networks ◽  
Environmental Data ◽  
Control Measures ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Analysis Strategy ◽  
Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

scholarly journals Hybrid forecasting model for non-linear time series

2020 ◽  
Author(s):  
E. Priyadarshini ◽  
G. Raj Gayathri ◽  
M. Vidhya ◽  
A. Govindarajan ◽  
Samuel Chakkravarthi
Keyword(s):  
Time Series ◽  
Linear Time ◽  
Forecasting Model ◽  
Non Linear ◽  
Hybrid Forecasting
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