This study introduces an Enhanced Autoregressive integrated moving average (E-ARIMA) model for anomaly detection in time-series data, using vibrations monitored by CA 202 accelerometers at the Kirkuk Gas Power Plant as a case study. The objective is to overcome the limitations of traditional ARIMA models in analyzing the non-linear and dynamic nature of industrial sensory data. The novel proposed methodology includes data preparation through linear interpolation to address dataset gaps, stationarity confirmation via the Augmented Dickey-Fuller Test, and ARIMA model optimization against the Akaike Information Criterion, with a specialized time-series cross-validation technique. The results show that E-ARIMA model has superior performance in anomaly detection compared to conventional Seasonal ARIMA (SARIMA) and Vector Autoregressive models. In this regard, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) criteria were utilized for this evaluation. Finally, the most important achievement of this research is that the results highlight the enhanced predictive accuracy of the E-ARIMA model, making it a potent tool for industrial applications such as machinery health monitoring, where early detection of anomalies is crucial to prevent costly downtimes and facilitate maintenance planning.

Fuzzy Autoregressive integrated moving average models are improved versions of the classic Autoregressive integrated moving average (ARIMA) models, proposed in order to overcome the data limitation of ARIMA models. In this paper, FARIMA models are combined with probabilistic classifiers in order to yield a more accurate model than FARIMA in financially incomplete data situations. Empirical results of using proposed hybrid model in exchange rate market forecasting indicate that the proposed model exhibits effectively improved forecasting accuracy. Thus, the proposed model can be used as an alternative to exchange rate forecasting tools, especially when the scant data is made available over a short span of time.

One of the traditional methods for forecasting is time series analyzing, which based on tow assumption: stationary and linearity. There is Suspicion to working by the traditional models. One of the substitute methods is Artificial Neural Network (ANN) which sometimes shows good potential ability for forecasting time series. In this paper, after reviewing the recent studies on ability of Autoregressive integrated moving average Process (ARIMA) and ANN, tow models for forecasting the exchange rate between march 2006 and February 2009 in Iran are compared. The results show that ANN has a better estimate than ARIMA. In this research, the MATLAB and Central bank data are utilized.

Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine (SVM) and Random Forest (RF) are proposed and analyzed for modelling and forecasting the Bitcoin price. While some of the proposed models are univariate, the other models are multivariate and as a result, the maximum, minimum and the opening daily price of Bitcoin are also used in these models. The proposed models are applied on the Bitcoin price from December 18, 2019 to March 1, 2020 and their performances are compared in terms of the performance measures of RMSE and MAPE by Diebold-Mariano statistical test. Based on RMSE and MAPE measures, the results show that SVM provides the best performance among all the models. In addition, ARIMA and Bayesian approaches outperform other univariate models where they provide smaller values for RMSE and MAPE.

Ajabshir plain is one of the most important agricultural areas in East Azarbaijan province, which groundwater resources were applied more than half the water requirement of Agricultural productions in this plain. Continuous utilization of groundwater caused a decline of 5. 5 m from since 1984. Therefore, optimal and sustainable exploitation of groundwater resources in this plain is a management necessity. Consequently, modeling and prediction of the exploitation process could be accomplished by an appropriate technique. This research was conducted with the aim of analyzing the groundwater level variations in Ajabshir plain with time series statistical models due to the ability of time series techniques to model and predict the behavior of temporal variation in water engineering. Also, in this study, the groundwater level decline was modeled for 16 years with 16 models. A Seasonal Autoregressive integrated moving average (SARIMA) was recognized as the most appropriate pattern. Modeling, testing and prediction model was as follows: about 50% of the data for modeling (from 1984 to 2006), 25% for the test (from 2006 to 2017) and 25% (from 2017 to 2028) was used for prediction. Results showed that the average volume of groundwater drop in the 11th year of the forecast (2028) is equal to 26. 5×106 m3 under continued exploitation with the current conditions for agricultural production in the plain. Different saving scenarios, including 0, 10, 15, 20, 25 and 30 percent savings starting from 2018 were considered for optimal management of groundwater application. In the eleventh year, about ×106 m3 of groundwater will be saved with savings of only 10% from 2018.

This article examines the forecast performance of ARFIMA and ARIMA models using data on daily stock price index of Tehran in period 25/11/2001 to 30/11/2011. To estimate the d parameter and other parameters, the NLS method in the software package Oxmetric / pcgive was used. After comparing the results of research models, ARFIMA models based on AIC, the model was found superior in modeling TEPIX. Also we use naive methods for estimating the prediction. Comparing the accuracy of the prediction models by criteria such as MAPFE and RMSFE and confidence intervals of the real values, we can deduce that the first Performance difference between the predicted long-term memory ARFIMA model is very minor compared to the ARIMA model And Secondly, inefficient ARFIMA model in Tehran capital market forecast is quite evident.

In terms of quality and quantity, Iranian saffron has a considerable position at the international level. We can significantly increase our export earnings from saffron by taking advantage of the existing capacity. It should be noted that sales forecasting based on time series analysis is a very important element for the design and implementation of marketing strategies in the international arena. However, the conventional approaches to forecasting which rely on ignoring the linear (or nonlinear) structure of data do not provide accurate results. Therefore, the main objective of this study is to design a hybrid model consisting of two methods, artificial neural networks (ANN) and Autoregressive integrated moving average (ARIMA), in order to use the unique features of the each of these methods and overcome the existing deficiencies. Using the data related to the export of Iranian saffron during the period of 1904-2013, the results of the study showed that the ARIMA-ANN hybrid model is stronger and has better performance than the ARIMA and ANN individual models in order to forecast Iranian saffron export. Therefore, given the considerable performance of the ARIMA-ANN hybrid model, the use of this model is recommended in developing strategies related to the export of saffron and also in the forecasting of variables for time series.

Introduction: The coronavirus disease 2019 (COVID-19) has become a public health concern, and behavioral adjustments will minimize its spread worldwide by 80%. The main purpose of this research was to examine the factors associated with concerns about COVID-19 and the future direction of the COVID-19 scenario of Bangladesh. Methods: The binary logistic regression model was performed to assess the impact of COVID-19 concern in Bangladesh. Based on data obtained through online surveys in November 2020 and to predict the next 40 days daily confirmed and deaths of COVID-19 in Bangladesh by applying the Autoregressive integrated moving average (ARIMA) model. Results: The study enrolled 400 respondents, with 253 (63. 2%) were male, and 147 (36. 8%) were female. The mean age of respondents was 25. 13 ± 5. 74 years old. Almost 70% of them were found to be concerned about the COVID-19 pandemic. The result showed that respondents’ education level, knowledge regarding COVID-19 transmits, households with aged people, seasonal flu and HD/respiratory problems, and materials used while sneezing/coughing significantly influenced COVID-19 concerns. The analysis predicted that confirmed cases would gradually decrease for the ARIMA model while death cases will be constant for the next 40 days in Bangladesh. Conclusion: The current study suggested that knowledge about COVID-19 spread and education played a vital role in the decline of COVID-19 concerned. A particular program should focus on creating an awareness of the disadvantages of concerns about the COVID-19 pandemic by augmenting knowledge about COVID-19 spread, enhancing Education in Bangladesh.