Time series forecasting using neural networks vs. Box- Jenkins methodology

Tang, Zhenmin; Almeida, C.; Fishwick, Paul A.

doi:10.1177/003754979105700508

Cited by 299 publications

(24 citation statements)

References 1 publication

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“…ANN is a decision-based machine learning technique which uses highly interconnected nodes to solve a particular problem [45]. It has been confirmed as a means for quantitative predictive modeling as ANN can handle dynamic, nonlinear, and noisy data.…”

Section: • Support Vector Regression (Svr)mentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

et al. 2019

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Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.

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Section: • Support Vector Regression (Svr)mentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

et al. 2019

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“…is the moving average operator and [17,18]. Box and Jenkins (1970) created the building blocks of ARIMA, breaking down the prediction process into three iterative steps: identification, estimation, and validation-as seen in Figure 1 [3,19,20]. The Integration value (d) is found in the identification process.…”

Section: Autoregressive Integrated Moving Average (Arima)mentioning

confidence: 99%

“…The next step of the Box-Jenkins process is the identification stage. The autocorrelation (AC) and partial autocorrelation (PAC) graphs were used to determine if there were any signs of trends within the series [20]. If they exist in the series, a higher order of differencing would be needed until the series appears to lose any trends.…”

Section: The Arima Model For Influent Forecastingmentioning

confidence: 99%

Influent Forecasting for Wastewater Treatment Plants in North America

Boyd

et al. 2019

Sustainability

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Autoregressive Integrated Moving Average (ARIMA) is a time series analysis model that can be dated back to 1955. It has been used in many different fields of study to analyze time series and forecast future data points; however, it has not been widely used to forecast daily wastewater influent flow. The objective of this study is to explore the possibility for wastewater treatment plants (WWTPs) to utilize ARIMA for daily influent flow forecasting. To pursue the objective confidently, five stations across North America are used to validate ARIMA’s performance. These stations include Woodward, Niagara, North Davis, and two confidential plants. The results demonstrate that ARIMA models can produce satisfactory daily influent flow forecasts. Considering the results of this study, ARIMA models could provide the operating engineers at both municipal and rural WWTPs with sufficient information to run the stations efficiently and thus, support wastewater management and planning at various levels within a watershed.

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“…In a further development, the NN models have been widely used for prediction or forecasting of time series data is real [20], [22], [19], [6], [16], [17], [29], and [15]. In addition, the application of NN models for time series analysis, especially in the field of time series econometrics, also encourages the development of several tests to test nonlinearity [28] and [14].…”

Section: Introductionmentioning

confidence: 99%

Fractional integrated recurrent neural network (FIRNN) for forecasting of time series data in electricity load in Java-Bali

Walid¹,

Subanar²,

Rosadi³

et al. 2015

ces

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The Increasing demand for electricity causes problems for State Electricity Company (SEC), which is called PLN in Indonesia in providing services to the public. Neural network (NN) is one of the methods that is often used in forecasting electricity load in different countries. Another form of neural network which is widely used for the analysis of issues that have a repeating pattern is the model of Recurrent Neural Network (RNN). Particularly, the problem in this paper is how to how to develop a model of Fractional Integrated Recurrent Neural Networks (FIRNN) in forecasting time series data on National Electricity Load and how are the forecasting results of time series data on national electricity load using Fractional Integrated Recurrent Neural Networks (FIRNN). Furthermore, RNN models in long memory nonlinear models in this study will be called Fractional Integrated Recurrent Neural Networks (FIRNN). This research was conducted with literature studies, simulations and applications to real cases in memory of long time series data, by taking the case of the burden of the use of electricity in Indonesia. The previous studies show that most of the time series on consumption patterns of electrical load in Semarang city shows the pattern of long memory, because it has a fractional difference parameter which can be seen in [26].

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Time series forecasting using neural networks vs. Box- Jenkins methodology

Cited by 299 publications

References 1 publication

Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

Influent Forecasting for Wastewater Treatment Plants in North America

Fractional integrated recurrent neural network (FIRNN) for forecasting of time series data in electricity load in Java-Bali

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