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1
Angers, Jean-François, Atanu Biswas, and Raju Maiti. "Bayesian Forecasting for Time Series of Categorical Data." Journal of Forecasting 36, no. 3 (May 9, 2016): 217–29. http://dx.doi.org/10.1002/for.2426.
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Kasyoki, Alexander. "Simple Steps for Fitting Arima Model to Time Series Data for Forecasting Using R." International Journal of Science and Research (IJSR) 4, no. 3 (April 5, 2015): 318–21. http://dx.doi.org/10.21275/sub151897.
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Hermansah, Hermansah, Dedi Rosadi, Abdurakhman Abdurakhman, and Herni Utami. "SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING." MEDIA STATISTIKA 13, no. 2 (December 28, 2020): 116–24. http://dx.doi.org/10.14710/medstat.13.2.116-124.
Повний текст джерелаАнотація:
NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best single model. Its application in this study was conducted on real data, such as the interest rate of Bank Indonesia. The comparison results of MASE, RMSE, and MAPE values with ARIMA and Exponential Smoothing models shows that the NARNN is the best model used to effectively improve forecasting accuracy.
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Phong, Pham Đinh. "A TIME SERIES FORECASTING MODEL BASED ON LINGUISTIC FORECASTING RULES." Journal of Computer Science and Cybernetics 37, no. 1 (March 29, 2021): 23–42. http://dx.doi.org/10.15625/1813-9663/37/1/15852.
Повний текст джерелаАнотація:
The fuzzy time series (FTS) forecasting models have been being studied intensively over the past few years. Most of the researches focus on improving the effectiveness of the FTS forecasting models using time-invariant fuzzy logical relationship groups proposed by Chen et al. In contrast to Chen’s model, a fuzzy set can be repeated in the right-hand side of the fuzzy logical relationship groups of Yu’s model. N. C. Dieu enhanced Yu’s forecasting model by using the time-variant fuzzy logical relationship groups instead of the time-invariant ones. The forecasting models mentioned above partition the historical data into subintervals and assign the fuzzy sets to them by the human expert’s experience. N. D. Hieu et al. proposed a linguistic time series by utilizing the hedge algebras quantification to converse the numerical time series data to the linguistic time series. Similar to the FTS forecasting model, the obtained linguistic time series can define the linguistic, logical relationships which are used to establish the linguistic, logical relationship groups and form a linguistic forecasting model. In this paper, we propose a linguistic time series forecasting model based on the linguistic forecasting rules induced from the linguistic, logical relationships instead of the linguistic, logical relationship groups proposed by N. D. Hieu. The experimental studies using the historical data of the enrollments of University of Alabama observed from 1971 to 1992 and the daily average temperature data observed from June 1996 to September 1996 in Taipei show the outperformance of the proposed forecasting models over the counterpart ones.
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Orzeszko, Witold. "Several Aspects of Nonparametric Prediction of Nonlinear Time Series." Przegląd Statystyczny 65, no. 1 (January 30, 2019): 7–24. http://dx.doi.org/10.5604/01.3001.0014.0522.
Повний текст джерелаАнотація:
Nonparametric regression is an alternative to the parametric approach, which consists of applying parametric models, i.e. models of the certain functional form with a fixed number of parameters. As opposed to the parametric approach, nonparametric models have a general form, which can be approximated increasingly precisely when the sample size grows. Hereby they do not impose such restricted assumptions about the form of the modelling dependencies and in consequence, they are more flexible and let the data speak for themselves. That is why they are a promising tool for forecasting, especially in case of nonlinear time series. One of the most popular nonparametric regression method is the Nadaraya- Watson kernel smoothing. Nowadays, there are a number of variations of this method, like the local-linear kernel estimator, which combines the local linear approximation and the kernel estimator. In the paper a Monte Carlo study is conducted in order to assess the usefulness of the kernel smoothers to nonlinear time series forecasting and to compare them with the other techniques of forecasting.
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Albara, Al-Khowarizmi, and Riyan Pradesyah. "Power Business Intelligence in the Data Science Visualization Process to Forecast CPO Prices." International Journal of Science, Technology & Management 2, no. 6 (November 20, 2021): 2198–208. http://dx.doi.org/10.46729/ijstm.v2i6.403.
Повний текст джерелаАнотація:
Forecasting is one of the techniques in data mining by utilizing the data available in the data warehouse. With the development of science, forecasting techniques have also entered the computational field where the forecasting technique uses the artificial neural network (ANN) method. Where is the method for simple forecasting using the Time Series method. However, the ability to create data visualizations certainly hinders researchers from maximizing research results. Of course, with the development of the Power BI software, the data science process is more neatly presented in the form of visualization, where the data science process involves various fields so that in this paper the results of forecasting the price of crude palm oil (CPO) are presented for the development of the CPO business with the hope of implementing the Business Process. intelligence (BI) by involving ANN, namely the time series for forecasting. From the final results, accuracy in forecasting with time series involves 2 accuracy techniques, the first using MAPE and getting a result of 0.03214% and the second using MSE to get 962.91 results.
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Belas, Andrii, and Petro Bidyuk. "Convolutional neural networks for modeling and forecasting nonlinear nonstationary processes." ScienceRise, no. 3 (June 30, 2021): 12–20. http://dx.doi.org/10.21303/2313-8416.2021.001924.
Повний текст джерелаАнотація:
The object of research. The object of research is modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data.
Investigated problem. There are several popular approaches to solving the problems of adequate model constructing and forecasting nonlinear nonstationary processes, such as autoregressive models and recurrent neural networks. However, each of them has its advantages and drawbacks. Autoregressive models cannot deal with the nonlinear or combined influence of previous states or external factors. Recurrent neural networks are computationally expensive and cannot work with sequences of high length or frequency.
The main scientific result. The model for forecasting nonlinear nonstationary processes presented in the form of the time series data was built using convolutional neural networks. The current study shows results in which convolutional networks are superior to recurrent ones in terms of both accuracy and complexity. It was possible to build a more accurate model with a much fewer number of parameters. It indicates that one-dimensional convolutional neural networks can be a quite reasonable choice for solving time series forecasting problems.
The area of practical use of the research results. Forecasting dynamics of processes in economy, finances, ecology, healthcare, technical systems and other areas exhibiting the types of nonlinear nonstationary processes.
Innovative technological product. Methodology of using convolutional neural networks for modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data.
Scope of the innovative technological product. Nonlinear nonstationary processes presented in the form of time-series data.
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JOSHI, BHAGAWATI P., and SANJAY KUMAR. "A COMPUTATIONAL METHOD FOR FUZZY TIME SERIES FORECASTING BASED ON DIFFERENCE PARAMETERS." International Journal of Modeling, Simulation, and Scientific Computing 04, no. 01 (December 27, 2012): 1250023. http://dx.doi.org/10.1142/s1793962312500237.
Повний текст джерелаАнотація:
Present study proposes a method for fuzzy time series forecasting based on difference parameters. The developed method has been presented in a form of simple computational algorithm. It utilizes various difference parameters being implemented on current state for forecasting the next state values to accommodate the possible vagueness in the data in an efficient way. The developed model has been simulated on the historical student enrollments data of University of Alabama and the obtained forecasted values have been compared with the existing methods to show its superiority. Further, the developed model has also been implemented in forecasting the movement of market prices of share of State Bank of India (SBI) at Bombay Stock Exchange (BSE), India.
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Ramli, Nazirah, Siti Musleha Ab Mutalib, and Daud Mohamad. "Fuzzy Time Series Forecasting Model based on Frequency Density and Similarity Measure Approach." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 281. http://dx.doi.org/10.14419/ijet.v7i4.30.22284.
Повний текст джерелаАнотація:
This paper proposes an enhanced fuzzy time series (FTS) prediction model that can keep some information under a various level of confidence throughout the forecasting procedure. The forecasting accuracy is developed based on the similarity between the fuzzified historical data and the fuzzy forecast values. No defuzzification process involves in the proposed method. The frequency density method is used to partition the interval, and the area and height type of similarity measure is utilized to get the forecasting accuracy. The proposed model is applied in a numerical example of the unemployment rate in Malaysia. The results show that on average 96.9% of the forecast values are similar to the historical data. The forecasting error based on the distance of the similarity measure is 0.031. The forecasting accuracy can be obtained directly from the forecast values of trapezoidal fuzzy numbers form without experiencing the defuzzification procedure.
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Murat, Małgorzata, Iwona Malinowska, Magdalena Gos, and Jaromir Krzyszczak. "Forecasting daily meteorological time series using ARIMA and regression models." International Agrophysics 32, no. 2 (April 1, 2018): 253–64. http://dx.doi.org/10.1515/intag-2017-0007.
Повний текст джерелаАнотація:
Abstract The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.
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KUMAR, ASHWANI, D. P. AGRAWAL, and S. D. JOSHI. "MULTISCALE NEUROFUZZY MODELS FOR FORECASTING IN TIME SERIES DATABASES." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 06 (November 2007): 859–78. http://dx.doi.org/10.1142/s0219691307002087.
Повний текст джерелаАнотація:
Multiscale neurofuzzy modeling combines the multiresolution property of the wavelet transform with the regression ability of neurofuzzy systems. A wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in detail and approximation. A neurofuzzy system is then trained on each of the relevant resolution scales (i.e. those scales where significant events are detected); and individual wavelet forecasts are recombined to form the overall forecast. The neurofuzzy models developed in this paper are based on Mamdani and Takagi–Sugeno–Kang approaches to the problem of fuzzy modeling based on the strategy knowledge expressed by the input-output data. Within these approaches, the proposed Neural-Fuzzy Inference System (NFIS) provides several methods that represent different alternatives in the fuzzy modeling process and how they can be integrated with the learning power of neural networks. Simulation results carried out on a forecasting problem associated with stock market, are included to demonstrate the potential of the proposed forecasting scheme.
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Audina, Bella, Mohamat Fatekurohman, and Abduh Riski. "Peramalan Arus Kas dengan Pendekatan Time Series Menggunakan Support Vector Machine." Indonesian Journal of Applied Statistics 4, no. 1 (May 30, 2021): 34. http://dx.doi.org/10.13057/ijas.v4i1.47953.
Повний текст джерелаАнотація:
<p>Cash flow is a form of financial report that is used as a measure of the company success in the investment world. So that companies need to forecast the cash flow to manage their finances. Statistics can be applied for the forecasting of cash flow using the <em>Support Vector Machine </em>(SVM) method on the time series data. The aim of this research is to determine the optimal parameter pair model of the <em>Radial Basic Function</em> kernel and to obtain the forecasting results of cash flow using the SVM method on the time series data. The independent variable is needed the data on cash flow from operating income, expenditure and investment expenditure, sum of all cash flow. While the dependent variable is the financial condition based on the <em>Free Cash Flow</em>. The result of this research is a model with the best parameter pairs of the SVM tuning results with the greatest accuracy that is 75%, 82%, 88%, 64% and the forecasting financial condition of PT Cakrawala for the next 16 months.</p><p><strong>Keywords: </strong>cash flow, forecasting, time series, support vector machine.</p>
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Hassani, Hossein, Mohammad Reza Yeganegi, Atikur Khan, and Emmanuel Sirimal Silva. "The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting." Signals 1, no. 1 (May 7, 2020): 4–25. http://dx.doi.org/10.3390/signals1010002.
Повний текст джерелаАнотація:
Data transformations are an important tool for improving the accuracy of forecasts from time series models. Historically, the impact of transformations have been evaluated on the forecasting performance of different parametric and nonparametric forecasting models. However, researchers have overlooked the evaluation of this factor in relation to the nonparametric forecasting model of Singular Spectrum Analysis (SSA). In this paper, we focus entirely on the impact of data transformations in the form of standardisation and logarithmic transformations on the forecasting performance of SSA when applied to 100 different datasets with different characteristics. Our findings indicate that data transformations have a significant impact on SSA forecasts at particular sampling frequencies.
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Abhishekh, Surendra Singh Gautam, and S. R. Singh. "A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series." New Mathematics and Natural Computation 14, no. 01 (March 2018): 91–111. http://dx.doi.org/10.1142/s1793005718500072.
Повний текст джерелаАнотація:
Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In this paper, we propose an enhanced and versatile method of forecasting using the concept of intuitionistic fuzzy time series (FTS) based on their score function. The developed method has been presented in the form of simple computational steps of forecasting instead of complicated max–min compositions operator of intuitionistic fuzzy sets to compute the relational matrix [Formula: see text]. Also, the proposed method is based on the maximum score and minimum accuracy function of intuitionistic fuzzy numbers (IFNs) to fuzzify the historical time series data. Further intuitionistic fuzzy logical relationship groups are defined and also provide a forecasted value and lies in an interval and is more appropriate rather than a crisp value. Furthermore, the proposed method has been implemented on the historical student enrollments data of University of Alabama and obtains the forecasted values which have been compared with the existing methods to show its superiority. The suitability of the proposed model has also been examined to forecast the movement of share market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE). The results of the comparison of MSE and MAPE indicate that the proposed method produces more accurate forecasting results.
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Nik Badrul Alam, Nik Muhammad Farhan Hakim, and Nazirah Ramli. "Time Series Forecasting Model Based on Intuitionistic Fuzzy Set via Equal Distribution of Hesitancy De-I-Fuzzification." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 06 (December 2021): 1015–29. http://dx.doi.org/10.1142/s0218488521500458.
Повний текст джерелаАнотація:
The fuzzy time series forecasting model is a powerful tool in forecasting the time series data. The nature of the fuzzy set exhibits its role in handling the uncertainty of the data. The intuitionistic fuzzy set (IFS) is a generalization of a fuzzy set that makes the forecasting process more precise and accurate. This paper proposes a new fuzzy forecasting model based on IFS via the de-i-fuzzification approach, namely equal distribution of hesitancy. The proposed model consists of four main parts; the fuzzification of historical data; the establishment of the IFS; the de-i-fuzzification; and the defuzzification. For the fuzzification, the historical data is partitioned into 14 intervals using the frequency density-based method and trapezoidal fuzzy numbers are used to fuzzify the data. The data are then converted into IFS. The data in IFS form is reduced to fuzzy set using equally distributed with the degree of hesitancy approach. The arithmetic rules based on centroid defuzzification is used to calculate the forecasted output. The proposed model shows a better performance than the existing forecasting models based on IFS, indicating that the equal distribution of hesitancy de-i-fuzzification managed to handle the non-determinism in the forecasting with simplified procedure. In the future, an improved method will be proposed to defuzzify the IFS into crisp values without going through the de-i-fuzzification process, yet preserving the nature of IFS.
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Eroshenko, Stanislav A., Alexandra I. Khalyasmaa, Denis A. Snegirev, Valeria V. Dubailova, Alexey M. Romanov, and Denis N. Butusov. "The Impact of Data Filtration on the Accuracy of Multiple Time-Domain Forecasting for Photovoltaic Power Plants Generation." Applied Sciences 10, no. 22 (November 21, 2020): 8265. http://dx.doi.org/10.3390/app10228265.
Повний текст джерелаАнотація:
The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days’ accurate and robust power generation forecasting. We provide a brief description of the piloted short-term forecasting system and place under close scrutiny the main sources of photovoltaic power plants’ generation forecasting errors. The effectiveness of the empirical approach versus unsupervised learning was investigated in application to source data filtration in order to improve the power generation forecasting accuracy for unstable weather conditions. The k-nearest neighbors’ methodology was justified to be optimal for initial data filtration, based on the clusterization results, associated with peculiar weather and seasonal conditions. The photovoltaic power plants’ forecasting accuracy improvement was further investigated for a one hour-ahead time-domain. It was proved that operational forecasting could be implemented based on the results of short-term day-ahead forecast mismatches predictions, which form the basis for multiple time-domain integrated forecasting tools. After a comparison of multiple time series forecasting approaches, operational forecasting was realized based on the second-order autoregression function and applied to short-term forecasting errors with the resulting accuracy of 87%. In the concluding part of the article the authors from the points of view of computational efficiency and scalability proposed the hardware system composition.
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Su, Liyun, and Chenlong Li. "Local Functional Coefficient Autoregressive Model for Multistep Prediction of Chaotic Time Series." Discrete Dynamics in Nature and Society 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/329487.
Повний текст джерелаАнотація:
A new methodology, which combines nonparametric method based on local functional coefficient autoregressive (LFAR) form with chaos theory and regional method, is proposed for multistep prediction of chaotic time series. The objective of this research study is to improve the performance of long-term forecasting of chaotic time series. To obtain the prediction values of chaotic time series, three steps are involved. Firstly, the original time series is reconstructed inm-dimensional phase space with a time delayτby using chaos theory. Secondly, select the nearest neighbor points by using local method in them-dimensional phase space. Thirdly, we use the nearest neighbor points to get a LFAR model. The proposed model’s parameters are selected by modified generalized cross validation (GCV) criterion. Both simulated data (Lorenz and Mackey-Glass systems) and real data (Sunspot time series) are used to illustrate the performance of the proposed methodology. By detailed investigation and comparing our results with published researches, we find that the LFAR model can effectively fit nonlinear characteristics of chaotic time series by using simple structure and has excellent performance for multistep forecasting.
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Muhammad, Mahadi, Sri Wahyuningsih, and Meiliyani Siringoringo. "Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee." Jambura Journal of Mathematics 1, no. 1 (January 2, 2021): 1–15. http://dx.doi.org/10.34312/jjom.v1i1.5940.
Повний текст джерелаАнотація:
ABSTRAKFuzzy time series (FTS) Lee adalah suatu metode peramalan yang digunakan ketika jumlah data historis yang tersedia sedikit, serta tidak mensyaratkan asumsi-asumsi tertentu yang harus terpenuhi. Metode ini menggunakan data historis berupa himpunan fuzzy yang berasal dari bilangan real atas himpunan semesta pada data aktual. FTS Lee adalah perkembangan dari FTS Song dan Chissom, FTS Cheng, serta FTS Chen. Pada penelitian ini dibahas penerapan FTS Lee pada data Nilai Tukar Petani Subsektor Peternakan (NTPT) di Kalimantan Timur. Tujuan penelitian ini adalah memperoleh hasil peramalan NTPT di Kalimantan Timur pada bulan Januari 2020 dengan menggunakan FTS Lee. Langkah awal dalam penelitian ini yaitu menentukan himpunan semesta pembicaraan, langkah kedua menentukan banyaknya himpunan fuzzy, langkah ketiga mendefinisikan derajat keanggotaan himpunan fuzzy terhadap dan melakukan fuzzyfikasi pada data aktual, langkah keempat membuat fuzzy logical relationship, langkah kelima membuat fuzzy logical relationship group, langkah keenam melakukan defuzzyfikasi sehingga diperoleh hasil peramalan, serta dilanjutkan dengan menghitung nilai mean absolute percentage error. Hasil penelitian menunjukkan bahwa peramalan menggunakan FTS Lee pada bulan Januari 2020 adalah 110,25. Nilai mean absolute percentage error pada hasil peramalan dengan menggunakan FTS Lee adalah sangat baik. ABSTRACTLee’s Fuzzy time series (FTS) is a forecasting method that is used when the number of historical data that available was small and does not require certain assumptions to be fulfilled. This method uses historical data in the form of fuzzy sets derived from real numbers over the set of universes in the actual data. FTS Lee is a development of FTS Song and Chissom, FTS Cheng, and FTS Chen. This research discusses the application of FTS Lee to the Exchange Rate of Farmers Subsectors Farm (ERFSF) in Kalimantan Timur. The purpose of this study was to obtain the results of ERFSF forecasting in Kalimantan Timur in January 2020 using FTS Lee. The first step during research is to determine the set of speech universes, the second step is to determine the number of fuzzy sets, the third step is to define the degree of fuzzy association membership and fuzzification on the actual data, the fourth step is to create a fuzzy logical relationship, the fifth step is to create a fuzzy logical relationship group, the sixth step is to perform defuzzification in order to obtain forecasting results, and continue by calculating the mean absolute percentage error value. The results showed that forecasting using FTS Lee in January 2020 was 110,25. The mean absolute percentage error value in forecasting results using FTS Lee is very good.
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Muhammad, Mahadi, Sri Wahyuningsih, and Meiliyani Siringoringo. "Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee." Jambura Journal of Mathematics 3, no. 1 (January 2, 2021): 1–15. http://dx.doi.org/10.34312/jjom.v3i1.5940.
Повний текст джерелаАнотація:
ABSTRAKFuzzy time series (FTS) Lee adalah suatu metode peramalan yang digunakan ketika jumlah data historis yang tersedia sedikit, serta tidak mensyaratkan asumsi-asumsi tertentu yang harus terpenuhi. Metode ini menggunakan data historis berupa himpunan fuzzy yang berasal dari bilangan real atas himpunan semesta pada data aktual. FTS Lee adalah perkembangan dari FTS Song dan Chissom, FTS Cheng, serta FTS Chen. Pada penelitian ini dibahas penerapan FTS Lee pada data Nilai Tukar Petani Subsektor Peternakan (NTPT) di Kalimantan Timur. Tujuan penelitian ini adalah memperoleh hasil peramalan NTPT di Kalimantan Timur pada bulan Januari 2020 dengan menggunakan FTS Lee. Langkah awal dalam penelitian ini yaitu menentukan himpunan semesta pembicaraan, langkah kedua menentukan banyaknya himpunan fuzzy, langkah ketiga mendefinisikan derajat keanggotaan himpunan fuzzy terhadap dan melakukan fuzzyfikasi pada data aktual, langkah keempat membuat fuzzy logical relationship, langkah kelima membuat fuzzy logical relationship group, langkah keenam melakukan defuzzyfikasi sehingga diperoleh hasil peramalan, serta dilanjutkan dengan menghitung nilai mean absolute percentage error. Hasil penelitian menunjukkan bahwa peramalan menggunakan FTS Lee pada bulan Januari 2020 adalah 110,25. Nilai mean absolute percentage error pada hasil peramalan dengan menggunakan FTS Lee adalah sangat baik. ABSTRACTLee’s Fuzzy time series (FTS) is a forecasting method that is used when the number of historical data that available was small and does not require certain assumptions to be fulfilled. This method uses historical data in the form of fuzzy sets derived from real numbers over the set of universes in the actual data. FTS Lee is a development of FTS Song and Chissom, FTS Cheng, and FTS Chen. This research discusses the application of FTS Lee to the Exchange Rate of Farmers Subsectors Farm (ERFSF) in Kalimantan Timur. The purpose of this study was to obtain the results of ERFSF forecasting in Kalimantan Timur in January 2020 using FTS Lee. The first step during research is to determine the set of speech universes, the second step is to determine the number of fuzzy sets, the third step is to define the degree of fuzzy association membership and fuzzification on the actual data, the fourth step is to create a fuzzy logical relationship, the fifth step is to create a fuzzy logical relationship group, the sixth step is to perform defuzzification in order to obtain forecasting results, and continue by calculating the mean absolute percentage error value. The results showed that forecasting using FTS Lee in January 2020 was 110,25. The mean absolute percentage error value in forecasting results using FTS Lee is very good.
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Grądz, Żaklin. "RESEARCH ON THE COMBUSTION PROCESS USING TIME SERIES." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 10, no. 2 (June 30, 2020): 52–55. http://dx.doi.org/10.35784/iapgos.1835.
Повний текст джерелаАнотація:
In the combustion process, one of the most important tasks is related to maintaining its stability. Numerous methods of monitoring, diagnostics, and analysis of the measurement data are used for this purpose. The information recorded in the combustion chamber constitute one-dimensional time series. In the case of non-stationary time series, which can be transformed into the stationary form, the autoregressive integrated moving average process can be employed. The paper presented the issue of forecasting the changes in flame luminosity. The investigations discussed in the work were carried out with the ARIMA model (p,d,q). The presented forecasts of changes in flame luminosity reflect the actual processes, which enables to employ them in diagnostics and control of the combustion process.
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Samia, Ayari, Nouira Kaouther, and Trabelsi Abdelwahed. "A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Air Quality in Urban Areas: Case of Tunisia." Advanced Materials Research 518-523 (May 2012): 2969–79. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.2969.
Повний текст джерелаАнотація:
Forecasting air quality time series represents a very difficult task since air quality contains autoregressive, linear and nonlinear patterns. Autoregressive Integrated Moving Average (ARIMA) models have been widely used in air quality time series forecasting. However, they fail to detect extreme events because of their presumed linear form of data. Artificial Neural Networks (ANN) models have proved to be promising nonlinear tools for air quality forecasting. A hybrid model combining ARIMA and ANN improved forecasting more than either of the models used independently. Experimental results with meteorological and Particulate Matter data indicated that the combined model can be used as an efficient forecasting and early warning system for providing air quality information towards the citizen, not only in Sfax Southern Suburbs but in other Tunisian regions that suffer from poor air quality conditions.
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Kajitani, Yoshio, Keith W. Hipel, and A. Ian Mcleod. "Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data." Journal of Forecasting 24, no. 2 (2005): 105–17. http://dx.doi.org/10.1002/for.940.
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Romanov, Anton A., and Aleksei A. Filippov. "AN APPROACH TO THE CONTEXTUAL ANALYSIS OF TIME SERIES." АВТОМАТИЗАЦИЯ ПРОЦЕССОВ УПРАВЛЕНИЯ 63, no. 1 (2021): 46–55. http://dx.doi.org/10.35752/1991-2927-2021-1-63-46-55.
Повний текст джерелаАнотація:
Forecasting methods despite their conventions and limitations are the evolution of descriptive analytics mechanisms. Any model of the real-world objects works only under conditions of restrictions and agreements. The same conclusion can be made for the forecasting process, that it is not possible to forecast future state of the researched objects for 100%. However, building the most accurate forecast under the given conditions is the key. Modern data mining methods are based on a variety of models. However, such models can’t define the components of researched objects and processes except those contained in their models. The context allows using additional domain knowledge in describing the behavior of objects and processes in the form of qualitative assessments of their state. The same dataset in different domains will have various models and analysis results. The article deals with an approach to the domain context formation based on the ontology for analyzing time series of industrial processes indicators. The logical representation of the ontology based on the ALCHI(D) descriptive logic is also considered. The article describes as well experimental results confirming the correctness and effectiveness of the approach proposed.
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Tang, Chen, and Yanlin Shi. "Forecasting High-Dimensional Financial Functional Time Series: An Application to Constituent Stocks in Dow Jones Index." Journal of Risk and Financial Management 14, no. 8 (July 23, 2021): 343. http://dx.doi.org/10.3390/jrfm14080343.
Повний текст джерелаАнотація:
Financial data (e.g., intraday share prices) are recorded almost continuously and thus take the form of a series of curves over the trading days. Those sequentially collected curves can be viewed as functional time series. When we have a large number of highly correlated shares, their intraday prices can be viewed as high-dimensional functional time series (HDFTS). In this paper, we propose a new approach to forecasting multiple financial functional time series that are highly correlated. The difficulty of forecasting high-dimensional functional time series lies in the “curse of dimensionality.” What complicates this problem is modeling the autocorrelation in the price curves and the comovement of multiple share prices simultaneously. To address these issues, we apply a matrix factor model to reduce the dimension. The matrix structure is maintained, as information contains in rows and columns of a matrix are interrelated. An application to the constituent stocks in the Dow Jones index shows that our approach can improve both dimension reduction and forecasting results when compared with various existing methods.
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Luu, Do Ngoc, Nguyen Ngoc Phien, and Duong Tuan Anh. "Tuning Parameters in Deep Belief Networks for Time Series Prediction through Harmony Search." International Journal of Machine Learning and Computing 11, no. 4 (August 2021): 274–80. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1047.
Повний текст джерелаАнотація:
There have been several researches of applying Deep Belief Networks (DBNs) to predict time series data. Most of these works pointed out that DBNs can bring out better prediction accuracy than traditional Artificial Neural Networks. However, one of the main shortcomings of using DBNs in time series prediction concerns with the proper selection of their parameters. In this paper, we investigate the use of Harmony Search algorithm for determining the parameters of DBN in forecasting time series. Experimental results on several synthetic and real world time series datasets revealed that the DBN with parameters selected by Harmony Search performs better than the DBN with parameters selected by Particle Swarm Optimization (PSO) or random method in most of the tested datasets.
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Mulesa, O. Yu, F. E. Geche, A. Ye Batyuk, and O. O. Melnyk. "Information technology for time series forecasting by the method of the forecast scheme synthesis." Ukrainian Journal of Information Technology 3, no. 2 (November 23, 2021): 81–86. http://dx.doi.org/10.23939/ujit2021.02.081.
Повний текст джерелаАнотація:
The study is devoted to the development of information technology for forecasting based on time series. It has been found that it is important to develop new models and forecasting methods to improve the quality of the forecast. Information technology is based on the evolutionary method of synthesis of the forecast scheme grounded on basic forecast models. The selected method allows you to consider any number of predictive models that may belong to different classes. For a given time series, the weight coefficients with which the models are included in the resulting forecast scheme are calculated by finding the solution to the optimization problem. The method of constructing the objective function for the optimization problem in the form of a linear combination of forecasting results by basic forecasting models is shown. It is proposed to find the solution to the optimization problem using a genetic algorithm. The result of the method is the forecast scheme, which is a linear combination of basic forecast models. To assess the quality of the forecast, it is suggested to use forecasting errors or forecast volatility calculated as the standard deviation. Forecast quality criteria are selected depending on the context of the task. The use of forecast volatility as a quality criterion, with repeated use of technology, will reduce the deviation of forecast values from real data. The structural scheme of information technology is developed. Structurally, information technology consists of two blocks: data processing and interpretation of the obtained values. The result of the application of the developed information technology is the production rules for determining the predicted value of the studied quantity. Experimental verification of the obtained results was performed. The problem of forecasting the number of religious organizations in Ukraine based on statistical data from 1997 to 2000 has been solved. The autoregression method and the linear regression model were chosen as the basic forecast models. Based on the results of using the developed information technology, the weights of the basic models were calculated. It is demonstrated that the obtained forecast scheme allowed to improve the average absolute percentage error and forecast volatility in comparison with the selected models. Keywords: information technology; time series; forecasting; evolutionary technologies; forecast volatility; synthesis of the forecast scheme.
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Fakhru Rosyad, Ardian, Farikhin, and Jatmiko Endro Suseno. "Information Systems of Forecasting Incidence Rates of Dengue Fever Disease Using Multivariate Fuzzy Time Series." E3S Web of Conferences 202 (2020): 14005. http://dx.doi.org/10.1051/e3sconf/202020214005.
Повний текст джерелаАнотація:
Demak Regency is one of the regions in Central Java Province with a low incidence of Dengue Fever compared to other cities and districts. Even so, DHF control needs to be done to minimize the occurrence of dengue fever, because DHF is a fairly dangerous disease. One form of controlling the number of DHF events that is widely used is using forecasting models, one of them is using Fuzzy Time Series. The Multivariate Fuzzy Time Series (MFTS) model is a development of the Fuzzy Time Series model that can be used to forecast using time series data by using more than one variable for forecasting, compared to the Fuzzy Time Series method that usually using only one variable. Based on the research results obtained, the MFTS model has a fairly accurate MAPE value, wherein the best MAPE was at 3 years scenario with MAPE 10,728%.
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Evita Purnaningrum. "Model Dynamic Ensemble Time Series untuk Prediksi Indeks Harga Saham Utama di Indonesia Pasca Pandemi." Majalah Ekonomi 26, no. 1 (July 19, 2021): 1–7. http://dx.doi.org/10.36456/majeko.vol26.no1.a3949.
Повний текст джерелаАнотація:
Forecasting or predicting stock prices in the form of time series data is still a hot topic consistently discussed in economic forums and financial markets. This article had been analyzed prediction of stock prices in Indonesia after experiencing a pandemic and one year after the Corona virus. This study had been applied a dynamic ensemble method that combines various prediction models to improve forecasting accuracy. The results showed that the model has a high level of accuracy with MAPE (Mean Absolute Percentage Error) values of 0.003714125, and RMSE (Root Mean Square Error) of 0.03958605. Furthermore, these results could be used as a basis for government policy making and stock investment decisions for investors.
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Lawnik, Marcin, and Arkadiusz Banasik. "Delphi Method Supported by Forecasting Software." Information 11, no. 2 (January 27, 2020): 65. http://dx.doi.org/10.3390/info11020065.
Повний текст джерелаАнотація:
The Delphi method is one of the basic tools for forecasting values in various types of issues. It uses the knowledge of experts, which is properly aggregated (e.g., in the form of descriptive statistics measures) and returns to the previous group of experts again, thus starting the next round of forecasting. The multi-stage prediction under the Delphi method allows for better stabilization of the results, which is extremely important in the process of forecasting. Experts in the forecasting process often have access to time series forecasting software but do not necessarily use it. Therefore, it seems advisable to add to the aggregate the value obtained using forecasting software. The advantage of this approach is in saving the time and costs of obtaining a forecast. That should be understood as a smaller burden on data analysts and the value of their work. According to the above mentioned key factors, the main contribution of the article is the use of a virtual expert in the form of a computer-enhanced mathematical tool, i.e., a programming library for a forecasting time series. The chosen software tool is the Prophet library—a Facebook tool that can be used in Python or R programming languages.
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Fleming, Sean W. "Artificial neural network forecasting of nonlinear Markov processes." Canadian Journal of Physics 85, no. 3 (March 1, 2007): 279–94. http://dx.doi.org/10.1139/p07-037.
Повний текст джерелаАнотація:
I assessed the performance characteristics of the feed-forward artificial neural network (ANN) as a first-order nonlinear Markov modelling technique. The ability to recover the underlying structure of five synthetic random time series was first tested. The method was then applied to an observed geophysical time series, and the results were compared against external empirical constraints and a simple representation of the underlying physics. The Monte Carlo experiments suggested that the ANN–Markov technique: (i) yields good prediction skill; (ii) in general, accurately retrieves the form of the iterative mapping, even for extremely noisy data; (iii) accomplishes the foregoing without any need to consider or adjust for the distributional characteristics of the data or driving noise; and (iv) accurately estimates the distribution of the strictly stochastic signal component. Application to a historical river-flow record again showed good forecast skill. Moreover, the robustness, flexibility, and simplicity of the method permitted easy identification of the fundamental nonlinear physical dynamics of this environmental system directly from the time series data, perhaps belying the common perception of ANNs as a strictly black-box prediction technique. The ANN–Markov technique may thus serve as a valuable data-driven tool for guiding the development of both process-based and parameteric statistical models. The lack of specific distributional assumptions and requirements notwithstanding, it was also found that manual distributional transformations may permit the method to be tuned to particular applications by emphasizing or de-emphasizing certain features of the data. Drawbacks to the method include substantial data-set length requirements, a general limitation of ANNs, as well as an inconsistent but potentially troubling tendency to partially imprint the form of the ANN activation function upon the estimated recursion relationship. PACS Nos.: 02.50.Ga, 05.10.–a, 05.45.Tp, 07.05.Mh, 02.50.Ey, 92.40.Fb
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KUMAR, ASHWANI, D. P. AGRAWAL, and S. D. JOSHI. "MULTIRESOLUTION FORECASTING FOR US RETAILING USING WAVELET DECOMPOSITIONS." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 04 (December 2003): 449–63. http://dx.doi.org/10.1142/s0219691303000281.
Повний текст джерелаАнотація:
In this paper we propose a simple forecasting strategy which exploits the multiresolution property of the wavelet transform. US aggregate retail sales data have strong trend and seasonal patterns, providing a good testing ground for the proposed forecasting method. First a wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in details and approximation. Then a forecasting engine (neural network or fuzzy inference system) is trained on each of the relevant resolution scales, and individual wavelet scale forecasts are recombined to form the overall forecast. Substantial information in both the dynamic nonlinear trend and seasonal patterns of the time series is efficiently exploited: we choose short past windows for the inputs to the forecasting engines at lower scales and long past windows at higher scales. The forecasting engines learn the mapping hierarchically: using a scale-recursive strategy, we combine only those scales where significant events are detected. Univariate simulation results on US aggregate retailing indicate that the proposed method fares favourably in relation to forecasting results obtained by training a neural network on original time series. Multivariate simulation results obtained by including structural components inflation, recession, interest rates, unemployment, show improvement in sales-trend forecast.
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Suradhaniwar, Saurabh, Soumyashree Kar, Surya S. Durbha, and Adinarayana Jagarlapudi. "Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies." Sensors 21, no. 7 (April 1, 2021): 2430. http://dx.doi.org/10.3390/s21072430.
Повний текст джерелаАнотація:
High-frequency monitoring of agrometeorological parameters is quintessential in the domain of Precision Agriculture (PA), where timeliness of collected observations and the ability to generate ahead-of-time predictions can substantially impact the crop yield. In this context, state-of-the-art internet-of-things (IoT)-based sensing platforms are often employed to generate, pre-process and assimilate real-time data from heterogeneous sensors and streaming data sources. Simultaneously, Time-Series Forecasting Algorithms (TSFAs) are responsible for generating reliable forecasts with a pre-defined forecast horizon and confidence. These TSFAs often rely on modelling the correlation between endogenous variables, the impact of exogenous variables on latent form and structural properties of data such as autocorrelation, periodicity, trend, pattern, and causality to approximate the model parameters. Traditionally, TSFAs such as the Holt–Winters (HW) and Autoregressive family of models (ARIMA) apply a linear and parametric approach towards model approximation, whilst models like Support Vector Regression (SVRs) and Neural Networks (NNs) adhere to a non-linear, non-parametric approach for modelling the historical data. Recently, Deep-Learning-based TSFAs such as Recurrent Neural Networks (RNNs), and Long-Short-Term-Memory (LSTMS) have gained popularity due to their capability to model long sequences of highly non-linear and stochastic data effectively. However, the evolution of TSFAs for predicting agrometeorological parameters pivots around one-step-ahead forecasting, which often overestimates the performance metrics defined for validating forecast capabilities of potential TSFAs. Hence, this paper attempts to evaluate and compare the performance of different machine learning (ML) and deep learning (DL) based TSFAs under one-step and multi-step-ahead forecast scenarios, thereby estimating the generalization capabilities of TSFA models over unseen data. The data used in this study are collected from an Automatic Weather Station (AWS), sampled at an interval of 15 min, and range over one month. Temperature (T) and Humidity (H) observations from the AWS are further converted into univariate, supervised time-series diurnal data profiles. Finally, walk-forward validation is used to evaluate recursive one-step-ahead forecasts until the desired prediction horizon is achieved. The results show that the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and SVR models outperform their DL-based counterparts in one-step and multi-step ahead settings with a fixed forecast horizon. This work aims to present a baseline comparison between different TSFAs to assist the process of model selection and facilitate rapid ahead-of-time forecasting for end-user applications.
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Nasution, M. Rizki, Dede Ruslan, and Ahmad Albar Tanjung. "PERAMALAN INFLASI DI INDONESIA: DYNAMIC MODEL AVERAGE." Media Ekonomi 28, no. 2 (May 30, 2021): 91–98. http://dx.doi.org/10.25105/me.v28i2.7085.
Повний текст джерелаАнотація:
This research is to forecast inflation in Indonesia on a national scale. Forecasting use in samples and out of samples as research. Converting results using the Dynamic Dynamic Model can give results. The estimation results are carried out in the BVAR form. In forecasting using time series data for the period 2010 to 2019. Forecasting with the value of RMSE is selected in the IHK_SAND variable and another variable IHK_PROD is accepted; INF; CPI_BM; IHK_PALGBB; IHK_KES; IHK_TKJK; and IHK_MJMRT.
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Belas, Oleg, and Andrii Belas. "General methods of forecasting nonlinear nonstationary processes based on mathematical models using statistical data." System research and information technologies, no. 1 (July 11, 2021): 79–86. http://dx.doi.org/10.20535/srit.2308-8893.2021.1.06.
Повний текст джерелаАнотація:
The article considers the problem of forecasting nonlinear nonstationary processes, presented in the form of time series, which can describe the dynamics of processes in both technical and economic systems. The general technique of analysis of such data and construction of corresponding mathematical models based on autoregressive models and recurrent neural networks is described in detail. The technique is applied on practical examples while performing the comparative analysis of models of forecasting of quantity of channels of service of cellular subscribers for a given station and revealing advantages and disadvantages of each method. The need to improve the existing methodology and develop a new approach is formulated.
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HASHEM-NAZARI, Mohammad, Akbar ESFAHANIPOUR, and S. M. T. FATEMI GHOMI. "NON-EQUIDISTANT “BASIC FORM”-FOCUSED GREY VERHULST MODELS (NBFGVMS) FOR ILL-STRUCTURED SOCIO-ECONOMIC FORECASTING PROBLEMS." Journal of Business Economics and Management 18, no. 4 (August 27, 2017): 676–94. http://dx.doi.org/10.3846/16111699.2017.1337045.
Повний текст джерелаАнотація:
Multiple uncertainties complicate socio-economic forecasting problems, especially when relying on ill-conditioned limited data. Such problems are best addressed by grey prediction models such as Grey Verhulst Model (GVM). This paper resolves the incompatibility between GVM’s estimation and prediction by taking its basic form equation as the basis of both. The resultant “Basic Form”-focused GVM (BFGVM) is also further developed to create Direct Non-equidistant BFGVM (DNBFGVM) and, in turn, DNBFGVM with Recursive simulation (DNBFGVMR). Experimental analyses comprise 19 socio-economic time series with an emphasis on Iranian population, a low-frequency non-equidistant time series with remarkable strategic importance. Promisingly, the proposed DNBFGVM and DNBFGVMR provide accurate in-sample and out-of-sample socio-economic forecasts, show highly significant improvements over the best traditional GVM, and offer cost-effective intelligent support of decision-making. Final results suggest future trends of studied socio-economic time series. Specifically, they reveal Iranian population to grow even slower than anticipated, demanding an urgent consideration of policy-makers.
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Wang, Ping, Xuran He, Hongyinping Feng, Guisheng Zhang, and Chenglu Rong. "A Hybrid Model for PM2.5 Concentration Forecasting Based on Neighbor Structural Information, a Case in North China." Sustainability 13, no. 2 (January 6, 2021): 447. http://dx.doi.org/10.3390/su13020447.
Повний текст джерелаАнотація:
PM2.5 concentration prediction is an important task in atmospheric environment research, so many prediction models have been established, such as machine learning algorithm, which shows remarkable generalization ability. The time series data composed of PM2.5 concentration have the implied structural characteristics such as the sequence characteristic in time dimension and the high dimension characteristic in dynamic-mode space, which makes it different from other research data. However, when the machine learning algorithm is applied to the PM2.5 time series prediction, due to the principle of input data composition, the above structural characteristics can not be fully reflected. In our study, a neighbor structural information extraction algorithm based on dynamic decomposition is proposed to represent the structural characteristics of time series, and a new hybrid prediction system is established by using the extracted neighbor structural information to improve the accuracy of PM2.5 concentration prediction. During the process of extracting neighbor structural information, the original PM2.5 concentration series is decomposed into finite dynamic modes according to the neighborhood data, which reflects the time series structural characteristics. The hybrid model integrates the neighbor structural information in the form of input vector, which ensures the applicability of the neighbor structural information and retains the composition form the original prediction system. The experimental results of six cities show that the hybrid prediction systems integrating neighbor structural information are significantly superior to the traditional models, and also confirm that the neighbor structural information extraction algorithm can capture effective time series structural information.
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Sivhugwana, K. S., and E. Ranganai. "Intelligent techniques, harmonically coupled and SARIMA models in forecasting solar radiation data: A hybridization approach." Journal of Energy in Southern Africa 31, no. 3 (October 20, 2020): 14–37. http://dx.doi.org/10.17159/2413-3051/2020/v31i3a7754.
Повний текст джерелаАнотація:
The unsteady and intermittent feature (mainly due to atmospheric mechanisms and diurnal cycles) of solar energy resource is often a stumbling block, due to its unpredictable nature, to receiving high-intensity levels of solar radiation at ground level. Hence, there has been a growing demand for accurate solar irradiance forecasts that properly explain the mixture of deterministic and stochastic characteristic (which may be linear or nonlinear) in which solar radiation presents itself on the earth’s surface. The seasonal autoregressive integrated moving average (SARIMA) models are popular for accurately modelling linearity, whilst the neural networks effectively capture the aspect of nonlinearity embedded in solar radiation data at ground level. This comparative study couples sinusoidal predictors at specified harmonic frequencies with SARIMA models, neural network autoregression (NNAR) models and the hybrid (SARIMA-NNAR) models to form the respective harmonically coupled models, namely, HCSARIMA models, HCNNAR models and HCSARIMA-NNAR models, with the sinusoidal predictor function, SARIMA, and NNAR parts capturing the deterministic, linear and nonlinear components, respectively. These models are used to forecast 10-minutely and 60-minutely averaged global horizontal irradiance data series obtained from the RVD Richtersveld solar radiometric station in the Northern Cape, South Africa. The forecasting accuracy of the three above-mentioned models is undertaken based on the relative mean square error, mean absolute error and mean absolute percentage error. The HCNNAR model and HCSARIMA-NNAR model gave more accurate forecasting results for 60-minutely and 10-minutely data, respectively.
Highlights
HCSARIMA models were outperformed by both HCNNAR models and HCSARIMA-NNAR models in the forecasting arena.
HCNNAR models were most appropriate for forecasting larger time scales (i.e. 60-minutely).
HCSARIMA-NNAR models were most appropriate for forecasting smaller time scales (i.e. 10-minutely).
Models fitted on the January data series performed better than those fitted on the June data series.
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Seçkin, Mine, Ahmet Çağdaş Seçkin, and Aysun Coşkun. "Production fault simulation and forecasting from time series data with machine learning in glove textile industry." Journal of Engineered Fibers and Fabrics 14 (January 2019): 155892501988346. http://dx.doi.org/10.1177/1558925019883462.
Повний текст джерелаАнотація:
Although textile production is heavily automation-based, it is viewed as a virgin area with regard to Industry 4.0. When the developments are integrated into the textile sector, efficiency is expected to increase. When data mining and machine learning studies are examined in textile sector, it is seen that there is a lack of data sharing related to production process in enterprises because of commercial concerns and confidentiality. In this study, a method is presented about how to simulate a production process and how to make regression from the time series data with machine learning. The simulation has been prepared for the annual production plan, and the corresponding faults based on the information received from textile glove enterprise and production data have been obtained. Data set has been applied to various machine learning methods within the scope of supervised learning to compare the learning performances. The errors that occur in the production process have been created using random parameters in the simulation. In order to verify the hypothesis that the errors may be forecast, various machine learning algorithms have been trained using data set in the form of time series. The variable showing the number of faulty products could be forecast very successfully. When forecasting the faulty product parameter, the random forest algorithm has demonstrated the highest success. As these error values have given high accuracy even in a simulation that works with uniformly distributed random parameters, highly accurate forecasts can be made in real-life applications as well.
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Et. al., Sumita Mukherjee,. "TIME SERIES MODELLING ALGORITHM FOR PREDICTION OF EARTHQUAKES IN NEPAL." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (March 21, 2021): 29–35. http://dx.doi.org/10.17762/itii.v9i2.302.
Повний текст джерелаАнотація:
The regression analysis, time series, logistic models, neural networks, the Bayesian belief network, and decision trees, are associated with big data, data mining research, algorithm not only to help forecasting but also used for prediction of earthquake. Seismic wave propagation in the form of the earth's crust, is responsible for earthquake occurrences and depends on associated variables and is to be determined from records obtained and received form Nepal Meteorology Department having different factors like depth, magnitude, location, latitude, longitude etc. by mining methods and results are then evaluated thoroughly. This paper focuses on implication of seismic pattern, trends, association, comparison of earthquake occurrences on statistical data, using time series mining. This paper also studies and correlates various factors with the seismic activity for predicting occurrences of earthquake by developing machine learning models through visualizing time series pattern. It recognizes a strong pattern and orchestrates earthquake prediction. The machine learning method used Python programming to generate accurate graphs neural networks modelling and explains overfitting, stationarity and parsimony features for earthquake prediction. Data is in a non-linear relationship uses curved fitting regression. In this study, the comparative results of seismic time series are analyzed at testing point and prediction point. To keep down the destruction by earthquake expert decision systems by Neural network can be developed only using seismic time series analysis having different factors which can be a good study and explanation to develop an algorithm using the following methodology Auto regression (AR), Moving Average (MA), Seasonal Autoregressive Integrated Moving-Average (SARIMA).
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Ouyang, Yicun, and Hujun Yin. "Multi-Step Time Series Forecasting with an Ensemble of Varied Length Mixture Models." International Journal of Neural Systems 28, no. 04 (March 12, 2018): 1750053. http://dx.doi.org/10.1142/s0129065717500538.
Повний текст джерелаАнотація:
Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model. They generally perform poorly in practical applications. In this paper, as an extension of the self-organizing mixture autoregressive (AR) model, the varied length mixture (VLM) models are proposed to model and forecast time series over multi-steps. The key idea is to preserve the dependencies between the time points within the prediction horizon. Training data are segmented to various lengths corresponding to various forecasting horizons, and the VLM models are trained in a self-organizing fashion on these segments to capture these dependencies in its component AR models of various predicting horizons. The VLM models form a probabilistic mixture of these varied length models. A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. The effectiveness of the proposed methods and their marked improvements over the existing methods are demonstrated through a number of experiments on synthetic data, real-world FX rates and weather temperatures.
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Wibisono, Dwi Anugrah, Dian Anggraeni, and Alfian Futuhul Hadi. "PERBAIKAN MODEL SEASONAL ARIMA DENGAN METODE ENSEMBLE KALMAN FILTER PADA HASIL PREDIKSI CURAH HUJAN." Majalah Ilmiah Matematika dan Statistika 19, no. 1 (March 12, 2019): 9. http://dx.doi.org/10.19184/mims.v19i1.17262.
Повний текст джерелаАнотація:
Forecasting is a time series analytic that used to find out upcoming improvement in the next event using past events as a reference. One of the forecasting models that can be used to predict a time series is Kalman Filter method. The modification of the estimation method of Kalman Filter is Ensemble Kalman Filter (EnKF). This research aims to find the result of EnKF algorithm implementation on SARIMA model. To start with, preticipation forecast data is changed in the form of SARIMA model to obtain some SARIMA model candidates. Next, this best model of SARIMA applied to Kalman Filter models. After Kalman Filter models created, forecasting could be done by applying pass rainfall data to the models. It can be used to predict rainfall intensity for next year. The quality of this forecasting can be assessed by looking at MAPE’s value and RMSE’s value. This research shows that enkf method relative can fix sarima method’s model, proved by mape and rmse values which are smaller and indicate a more accurate prediction.
Keywords: Ensemble Kalman Filter, Forecast, SARIMA
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Glaser, Sarah M., Hao Ye, Mark Maunder, Alec MacCall, Michael Fogarty, and George Sugihara. "Detecting and forecasting complex nonlinear dynamics in spatially structured catch-per-unit-effort time series for North Pacific albacore (Thunnus alalunga)." Canadian Journal of Fisheries and Aquatic Sciences 68, no. 3 (March 2011): 400–412. http://dx.doi.org/10.1139/f10-160.
Повний текст джерелаАнотація:
The presence of complex, nonlinear dynamics in fish populations, and uncertainty in the structure (functional form) of those dynamics, pose challenges to the accuracy of forecasts produced by traditional stock assessment models. We describe two nonlinear forecasting models that test for the hallmarks of complex behavior, avoid problems of structural uncertainty, and produce good forecasts of catch-per-unit-effort (CPUE) time series in both standardized and nominal (unprocessed) form. We analyze a spatially extensive, 40-year-long data set of annual CPUE time series of North Pacific albacore ( Thunnus alalunga ) from 1° × 1° cells from the eastern North Pacific Ocean. The use of spatially structured data in compositing techniques improves out-of-sample forecasts of CPUE and overcomes difficulties commonly encountered when using short, incomplete time series. These CPUE series display low-dimensional, nonlinear structure and significant predictability. Such characteristics have important implications for industry efficiency in terms of future planning and can inform formal stock assessments used for the management of fisheries.
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Pratama, Yunidar Ayu, and Diah Indriani. "Peramalan KB Baru IUD dengan Metode Automatic Clustering and Fuzzy Logical Relationship." Jurnal Biometrika dan Kependudukan 6, no. 2 (October 30, 2018): 144. http://dx.doi.org/10.20473/jbk.v6i2.2017.144-153.
Повний текст джерелаАнотація:
This research aims for forecasting the number of participants Family Planning (FP) new IUD in East Java in 2017 method using Automatic Clustering And Fuzzy Logic Relationship (ACFLR). Make forecasting for the number of participants FP new IUD in the future important done. Forecasting will support the increase of the number of participants program FP new IUD as emphasized by the Government so that it can be used to take better decisions. Forecasting method of Automatic Clustering And Fuzzy Logical Relationship was chosen because the method has a higher degree of accuracy compared to the classical time series method and fuzzy time series. This study used secondary data recorded in Perwakilan BKKBN East Java in the form of the number of participants KB new IUD in East Java in 2011 to 2016. Based on the research results obtained forecasting the number of participants KB new IUD in 2017 is 65.616 participants with error rate prediction of 0.97% and the percentage increase in the number of participants from the previous year is 0.28%.
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Jurnal, Redaksi Tim. "STUDI PERAMALAN BEBAN RATA – RATA JANGKA PENDEK MENGGUNAKAN METODA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA." Sutet 7, no. 2 (November 27, 2018): 93–101. http://dx.doi.org/10.33322/sutet.v7i2.84.
Повний текст джерелаАнотація:
Forecasting. Plans, power plants ,. Electricity needs are increasingly changing daily, so the State Electricity Company (PLN) as a provider of energy must be able to predict daily electricity needs. Short-term forecasting is the prediction of electricity demand for a certain period of time ranging from a few minutes to a week ahead. in shortterm electrical forecasting much of the literature describes the techniques and methods applied in forecasting, Autoregresive Integrated Moving Average (ARIMA), linear regression, and artificial intelligence such as Artificial Neural Networks and fuzzy logic. Short-term forecasting will be done by the authors using time series data that is the data of the use of electric power daily (electrical load) and ARIMA as a method of forecasting. ARIMA method or often called Box-Jenkins technique to find this method is suitable to predict variable costs quickly, simply, and cheaply because it only requires data variables to be predicted. ARIMA can only be used for short-term forecasting. ARIMA is a special linear test, in the form of forecasting this model is completely independent variable variables because this model uses the current model and past values of the dependent variable to produce an accurate short-term forecast.
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Perwira, Rifki Indra, Danang Yudhiantoro, and Endah Wahyurini. "FUZZY TIME SERIES MODEL CHENG UNTUK MERAMALKAN VOLUME HASIL PANEN PADA TANAMAN GARUT." Telematika 17, no. 1 (April 30, 2020): 11. http://dx.doi.org/10.31315/telematika.v17i1.3400.
Повний текст джерелаАнотація:
Arrowroot is an alternative food substitute that can be used as processed flour or starch. This arrowroot can also produce several processed products such as arrowroot chips. The number of arrowroot requests from various regions causes the need for accurate calculations related to the volume of harvest from the arrowroot. Fuzzy logic is a method that can be used to predict arrowroot yields every period to meet market demand. The parameters used in this system are based on environmental data (temperature humidity, climate, altitude), genetic data (age and variety), and cultivation technique data (seed quality, fertilizing, planting media). The results of this study are in the form of an application to predict the volume of arrowroot crop yields based on these parameters. From the results of MAPE, get a percentage of 11.7% which indicates that the level of accuracy using the fuzzy cheng time series model is said to be useful for forecasting on arrowroot plants.
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Boudhaouia, Aida, and Patrice Wira. "A Real-Time Data Analysis Platform for Short-Term Water Consumption Forecasting with Machine Learning." Forecasting 3, no. 4 (September 26, 2021): 682–94. http://dx.doi.org/10.3390/forecast3040042.
Повний текст джерелаАнотація:
This article presents a real-time data analysis platform to forecast water consumption with Machine-Learning (ML) techniques. The strategy fully relies on a web-oriented architecture to ensure better management and optimized monitoring of water consumption. This monitoring is carried out through a communicating system for collecting data in the form of unevenly spaced time series. The platform is completed by learning capabilities to analyze and forecast water consumption. The analysis consists of checking the data integrity and inconsistency, in looking for missing data, and in detecting abnormal consumption. Forecasting is based on the Long Short-Term Memory (LSTM) and the Back-Propagation Neural Network (BPNN). After evaluation, results show that the ML approaches can predict water consumption without having prior knowledge about the data and the users. The LSTM approach, by being able to grab the long-term dependencies between time steps of water consumption, allows the prediction of the amount of consumed water in the next hour with an error of some liters and the instants of the 5 next consumed liters in some milliseconds.
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Alsudani, Rana Sabeeh Abood, Jicheng Liu, and Zahrah Ismael Salman. "Forecasting mortality patterns of thalassaemia major patients in Iraq by using VAR model and reasons for this mortality." JOURNAL OF ADVANCES IN MATHEMATICS 12, no. 11 (December 30, 2016): 6785–98. http://dx.doi.org/10.24297/jam.v12i11.18.
Повний текст джерелаАнотація:
The vector autoregression model (VAR) is a natural extension of the univariate autoregressive model dynamic multivariable time series. It is one of the most successful, flexible, and easy to use models for the analysis of multivariable time series. The VAR model has proved to be particularly useful describing the dynamic behaviour of economic and financial time series and forecasting. Often it provides superior forecasts to those of time-series models and univariate and detailed forecasts, based on the theory of simultaneous equation models. Expectations of VAR models are very flexible because they can be conditioned on possible paths for the future in the form of specific variables. In addition to describing the data and forecasting, the VAR model is used to deduce structural and policy analysis. This study used the VAR model for forecasting the number of deaths in patients with thalassemia in Maysan province in southern Iraq, and also addressed the causes of these deaths. There was a strong relationship between mortality in thalassemia patients and an increase in the proportion of iron and the highest number of deaths was recorded for patients who had a very high proportion of iron. It was „the most important cause of mortality (Cardiac disease, infections, the liver, the spleen).
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Vrbka, Jaromír, and Marek Vochozka. "Considering seasonal fluctuations on balancing time series with the use of artificial neural networks when forecasting US imports from the PRC." SHS Web of Conferences 73 (2020): 01033. http://dx.doi.org/10.1051/shsconf/20207301033.
Повний текст джерелаАнотація:
The paper’s objective is to propose a particular methodology to be used to regard seasonal fluctuations on balancing time series while using artificial neural networks based on the example of imports from the People's Republic of China (PRC) to the USA (US). The difficulty of forecasting the volume of foreign trade is usually given by the limitations of many conventional forecasting models. For the improvement of forecasting it is necessary to propose an approach that would hybridize econometric models and artificial intelligence models. Data for an analysis to be conducted are available on the World Bank website, etc. Information on US imports from the PRC will be used. Each forecast is given by a certain degree of probability which it will be fulfilled with. Although it appeared before the experiment that there was no reason to include the categorical variable to reflect seasonal fluctuations of the USA imports from the PRC, the assumption was not correct. An additional variable, in the form of monthly value measurements, brought greater order and accuracy to the balanced time series.
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Chaban, V. I., S. P. Kliavzo, O. U. Podobed, and S. A. Chernyh. "Sunflower yield forecast using ARIMA time series models." Scientific Journal Grain Crops 5, no. 2 (2022): 267–74. http://dx.doi.org/10.31867/2523-4544/0185.
Повний текст джерелаАнотація:
The forecast of sunflower yield was based on the analysis of the time series of yield data of this crop at its cultivation in the Northern Steppes of Ukraine against the background of natural fertility for 1971-2019. The true average yield value of sunflower ranged from 2.15 ± 0.17 t/ha, the average variation of yield data for the study period was: coefficient of variation – Cv = 24 %, standard deviation – s = 0.516 t/ha. Analysis of the scattering graph of the series showed a tendency to increase the sunflower yield over a given period of time. An adequate linear model with an increasing trend of yield data is obtained. According to the forecast results by this method for the period up to 2025, the sunflower yield is expected at the level of 2.59–2.67 t/ha. Forecasting with ARIMA (Autoregressive Integrated Moving Average) was carried out by reduction of the yield data series to a stationary form, which was achieved by first order differentiation D (-1). The selection of the most adaptive model was carried out by varying the values of p and q, according to the type of autocorrelation (ACF) and partial autocorrelation functions (PACF). It was found that the best model is D (-1) ARIMA model: (2,0,0), the stationarity of which was achieved by first order differentiation, the residuals are not autocorrelated and normally distributed, and the regression coefficients corresponded to the values of residual probabilities less (p <0, 05). According to the short-term forecast based on the chosen model, it was found that the maximum of sunflower yield against the background of natural fertility in 2023 should be expected up to 3.56 t/ha. Keywords: forecast, yield, sunflower, model, time series, ARIMA model.
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Maulana, Hutomo Atman, Kasuma Wardany Harahap, Adriyansyah Adriyansyah, Rofiroh Rofiroh, and Fuad Zainuddin. "Permodelan Produksi Kopi Indonesia dengan Menggunakan Seasonal Autoregressive Integrated Moving Average (SARIMA)." Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam 2, no. 1 (August 1, 2019): 1. http://dx.doi.org/10.32493/jsmu.v2i1.2914.
Повний текст джерелаАнотація:
This research used a method in modelling time series data in the form of seasonal data. The method used in this study is the Seasonal Autoregressive Integrated Moving Average (SARIMA). This method is applied to Indonesian coffee production data from January 2009 - December 2013 with the aim of obtaining a model that will be used to predict the amount of coffee production in January 2014 - December 2014. The forecasting results from the next model will be compared with the original data. Data processing is done using EViews software. Based on the results of data processing, the best model for forecasting is obtained, SARIMA (2,1,0) (1,1,1)12