scholarly journals A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3299
Author(s):  
Ashish Shrestha ◽  
Bishal Ghimire ◽  
Francisco Gonzalez-Longatt
Keyword(s):  
Time Series ◽  
Kinetic Energy ◽  
Power System ◽  
Bayesian Model ◽  
Arima Model ◽  
Power Converter ◽  
Series Data ◽  
Percentage Error ◽  
Optimal Size ◽  
Short Term

Withthe massive penetration of electronic power converter (EPC)-based technologies, numerous issues are being noticed in the modern power system that may directly affect system dynamics and operational security. The estimation of system performance parameters is especially important for transmission system operators (TSOs) in order to operate a power system securely. This paper presents a Bayesian model to forecast short-term kinetic energy time series data for a power system, which can thus help TSOs to operate a respective power system securely. A Markov chain Monte Carlo (MCMC) method used as a No-U-Turn sampler and Stan’s limited-memory Broyden–Fletcher–Goldfarb–Shanno (LM-BFGS) algorithm is used as the optimization method here. The concept of decomposable time series modeling is adopted to analyze the seasonal characteristics of datasets, and numerous performance measurement matrices are used for model validation. Besides, an autoregressive integrated moving average (ARIMA) model is used to compare the results of the presented model. At last, the optimal size of the training dataset is identified, which is required to forecast the 30-min values of the kinetic energy with a low error. In this study, one-year univariate data (1-min resolution) for the integrated Nordic power system (INPS) are used to forecast the kinetic energy for sequences of 30 min (i.e., short-term sequences). Performance evaluation metrics such as the root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE) of the proposed model are calculated here to be 4.67, 3.865, 0.048, and 8.15, respectively. In addition, the performance matrices can be improved by up to 3.28, 2.67, 0.034, and 5.62, respectively, by increasing MCMC sampling. Similarly, 180.5 h of historic data is sufficient to forecast short-term results for the case study here with an accuracy of 1.54504 for the RMSE.

scholarly journals ARIMAX Model for Short-Term Electrical Load Forecasting

2019 ◽  
Vol 8 (4) ◽  
pp. 2786-2790
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Load Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Electrical Load ◽  
Short Term ◽  
Exogenous Variables ◽  
Time Series Modelling ◽  
Electrical Load Forecasting

The scope for ARIMAX approach to forecast short term load has gained a lot of significant importance.In this paper, ARIMAXmodel which is an extension of ARIMA model with exogenous variables is used for STLF on a time series data of Karnataka State Demand pattern. The forecasting accuracy of ARIMA model is enhanced by taking into consideration hour of the day and day of the week as exogenous variables for ARIMAX model. Forecasting performance is thus improved by considering these significant load dependent factors. The forecasted results indicate that the proposed model is more accurate according to mean absolute percentage error (MAPE) obtained during the testing period of the model. As the historical load data are available on the databases of the utility, researches in the areas of time series modelling are ongoing for electrical load forecasting. In the proposed paper real time demand data available on Karnataka Power Transmission Corporation Ltd. (KPTCL) website is taken to develop and test the proposedload forecasting model.The power utility system operational costs and its securitydepend on the load forecasting for next few hours. Regional load forecasting helps in the accurate management performance of generation of power plant. Today’s deregulated markets have great demand for prediction of electrical loads, required for generating companies. There has been tremendous growth in electric power demand and hence it is very much essentialfor the utility sectors to have theirdemand information in advance.

scholarly journals Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yihuai Huang ◽  
Chao Xu ◽  
Mengzhong Ji ◽  
Wei Xiang ◽  
Da He
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Adaptive Filtering ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Short Term ◽  
Hybrid Prediction ◽  
Service Demand ◽  
Filtering Method

Abstract Background Accurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation. The daily outpatient volume is characterized by randomness, periodicity and trend, and the time series methods, like ARIMA are often used for short-term outpatient visits forecasting. Therefore, to further enlarge the prediction horizon and improve the prediction accuracy, a hybrid prediction model integrating ARIMA and self-adaptive filtering method is proposed. Methods The ARIMA model is first used to identify the features like cyclicity and trend of the time series data and to estimate the model parameters. The parameters are then adjusted by the steepest descent algorithm in the adaptive filtering method to reduce the prediction error. The hybrid model is validated and compared with traditional ARIMA by several test sets from the Time Series Data Library (TSDL), a weekly emergency department (ED) visit case from literature study, and the real cases of prenatal examinations and B-ultrasounds in a maternal and child health care center (MCHCC) in Ningbo. Results For TSDL cases the prediction accuracy of the hybrid prediction is improved by 80–99% compared with the ARIMA model. For the weekly ED visit case, the forecasting results of the hybrid model are better than those of both traditional ARIMA and ANN model, and similar to the ANN combined data decomposition model mentioned in the literature. For the actual data of MCHCC in Ningbo, the MAPE predicted by the ARIMA model in the two departments was 18.53 and 27.69%, respectively, and the hybrid models were 2.79 and 1.25%, respectively. Conclusions The hybrid prediction model outperforms the traditional ARIMA model in both accurate predicting result with smaller average relative error and the applicability for short-term and medium-term prediction.

scholarly journals Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network

2017 ◽  
Vol 145 (6) ◽  
pp. 1118-1129 ◽  
Author(s):  
K. W. WANG ◽  
C. DENG ◽  
J. P. LI ◽  
Y. Y. ZHANG ◽  
X. Y. LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Prevention And Control ◽  
Time Series Data ◽  
Arima Model ◽  
Public Health Problem ◽  
Series Data ◽  
Percentage Error ◽  
And Control

SUMMARYTuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

scholarly journals Time Series Analysis and Forecasting of Oilseeds Production in India: Using Autoregressive Integrated Moving Average and Group Method of Data Handling – Neural Network

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Debasis Mithiya ◽  
Lakshmikanta Datta ◽  
Kumarjit Mandal
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Group Method ◽  
Data Handling ◽  
Agricultural Economy ◽  
Autoregressive Integrated Moving Average

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.

scholarly journals Self-Identification ResNet-ARIMA Forecasting Model

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Model Identification ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Partial Autocorrelation Function

The challenging endeavor of a time series forecast model is to predict the future time series data accurately. Traditionally, the fundamental forecasting model in time series analysis is the autoregressive integrated moving average model or the ARIMA model requiring a model identification of a three-component vector which are the autoregressive order, the differencing order, and the moving average order before fitting coefficients of the model via the Box-Jenkins method. A model identification is analyzed via the sample autocorrelation function and the sample partial autocorrelation function which are effective tools for identifying the ARMA order but it is quite difficult for analysts. Even though a likelihood based-method is presented to automate this process by varying the ARIMA order and choosing the best one with the smallest criteria, such as Akaike information criterion. Nevertheless the obtained ARIMA model may not pass the residual diagnostic test. This paper presents the residual neural network model, called the self-identification ResNet-ARIMA order model to automatically learn the ARIMA order from known ARIMA time series data via sample autocorrelation function, the sample partial autocorrelation function and differencing time series images. In this work, the training time series data are randomly simulated and checked for stationary and invertibility properties before they are used. The result order from the model is used to generate and fit the ARIMA model by the Box-Jenkins method for predicting future values. The whole process of the forecasting time series algorithm is called the self-identification ResNet-ARIMA algorithm. The performance of the residual neural network model is evaluated by Precision, Recall and F1-score and is compared with the likelihood basedmethod and ResNET50. In addition, the performance of the forecasting time series algorithm is applied to the real world datasets to ensure the reliability by mean absolute percentage error, symmetric mean absolute percentage error, mean absolute error and root mean square error and this algorithm is confirmed with the residual diagnostic checks by the Ljung-Box test. From the experimental results, the new methodologies of this research outperforms other models in terms of identifying the order and predicting the future values.

scholarly journals Implementation of Long Short-Term Memory and Gated Recurrent Units on Grouped Time-Series Data to Predict Stock Prices Accurately

2021 ◽  
Author(s):  
Armin Lawi ◽  
Hendra Mesra ◽  
Supri Amir
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Percentage Error ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract Stocks are an attractive investment option since they can generate large profits compared to other businesses. The movement of stock price patterns on the stock market is very dynamic; thus it requires accurate data modeling to forecast stock prices with a low error rate. Forecasting models using Deep Learning are believed to be able to accurately predict stock price movements using time-series data, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. However, several previous implementation studies have not been able to obtain convincing accuracy results. This paper proposes the implementation of the forecasting method by classifying the movement of time-series data on company stock prices into three groups using LSTM and GRU. The accuracy of the built model is evaluated using loss functions of Rooted Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results showed that the performance evaluation of both architectures is accurate in which GRU is always superior to LSTM. The highest validation for GRU was 98.73% (RMSE) and 98.54% (MAPE), while the LSTM validation was 98.26% (RMSE) and 97.71% (MAPE).

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.

Modeling of a simplified hybrid algorithm for short-term load forecasting in a power system network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kathiresh Mayilsamy ◽  
Maideen Abdhulkader Jeylani A, ◽  
Mahaboob Subahani Akbarali ◽  
Haripranesh Sathiyanarayanan
Keyword(s):  
Time Series ◽  
Hybrid Algorithm ◽  
Hybrid Approach ◽  
Load Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Short Term ◽  
Content Type ◽  
Highly Nonlinear ◽  
Short Term Load Forecasting

Purpose The purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for addressing the non-linearity of the load time series. Design/methodology/approach Short-term load forecasting is a complex process as the nature of the load-time series data is highly nonlinear. So, only ARIMA-based load forecasting will not provide accurate results. Hence, ARIMA is combined with MLP, a deep learning approach that models the resultant data from ARIMA and processes them further for Modelling the non-linearity. Findings The proposed hybrid approach detects the residuals of the ARIMA, a linear statistical technique and models these residuals with MLP neural network. As the non-linearity of the load time series is approximated in this error modeling process, the proposed approach produces accurate forecasting results of the hourly loads. Originality/value The effectiveness of the proposed approach is tested in the laboratory with the real load data of a metropolitan city from South India. The performance of the proposed hybrid approach is compared with the conventional methods based on the metrics such as mean absolute percentage error and root mean square error. The comparative results show that the proposed prediction strategy outperforms the other hybrid methods in terms of accuracy.

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