Showing 13 results for Time Series
Volume 3, Issue 3 (9-2015)
Abstract
The present research was planned to evaluate the skill of linear stochastic models known as ARIMA and multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) model in the quantitative forecasting of the Standard Runoff Index (SRI) in Karkheh Basin. To this end, SRI was computed in monthly and seasonal time scales in 10 hydrometric stations in 1974-75 to 2012-13 period of time and then the modeling of SRI time series was done to forecast the one to six months of lead-time and up to two seasons of lead-time. The SRI values related to 1974-75 to 1999-2000 were used to develop the model and the residual data (2000-2001 to 2012-13) were used in model validation. In the validation stage, the observed and the predicted values of SRI were compared using correlation coefficient, error criteria and statistical tests. Finally, models skills were determined in view point of forecasting of lead-time and the time scale of drought evaluation. Results showed that the model accuracy in forecasting two months and one season of lead-time was high. In terms of the forecasting of SRI values, the skill of SARIMA in monthly time scale (with a RMSE and a MAE of 0.61 and 0.45 respectively and a correlation coefficient average of 0.72) was better than its skill in seasonal time scale. The application of SARIMA in monthly time scale was therefore preferred to its application in seasonal time scale.
Volume 8, Issue 4 (9-2020)
Abstract
Aims: The main objective of the current study was to assess the efficiency of four-time series prediction methods to forecast the values of total dissolved solids (TDS) using a time series of over sixteen years.
Materials & Methods: The applied methods comprised of autoregressive integrated moving average (ARIMA) as the most traditional method, two neural network based techniques including multilayer perceptron (MLP) along with extreme learning machines (ELM) and a novel approach known as temporal hierarchies (TH) which was applied for the first time in water resources and water quality researches.
Findings: It was found that with respect to the forecasting accuracy, the MLP outperforms the ARIMA model for the training series where the MAPE (%) and MASE (mg/l) were reduced from 5.109 to 3.146 and 0.553 to 0.323, respectively. On the other hand, the forecasting accuracy of ELM was lower than that of MLP however the respective out-of-sample generalization ability of this model was higher with MAPE and MASE values of 6.526 and 0.683.
Conclusion: Meanwhile, it was concluded that temporal hierarchies gave the best results for the test part of time series. The main shortcoming of neural network based approaches was their reduced out-of-sample prediction due to overfitting. Based on the results, TH is a viable alternative for conventional time series forecasting techniques.
Mohammad Ali Moradi,
Volume 10, Issue 2 (7-2010)
Abstract
Since the first oil shock in 1973, almost the economic performance of Iran has been related to its natural resource wealth. The economy has experienced relatively lower per capita GDP growth and higher income inequality. This may support this hypothesis that natural resources seem to have been more of a curse than a blessing for Iran.
This paper aims to analyze the effects of oil resource abundance on two major macroeconomic variables, economic growth and income distribution, in Iran using the data over the period 1968 - 2005. I take a time series perspective and focus on major forces of economic growth including oil resource. Moreover, the main determinants of income distribution are theoretically specified to examine the effects of oil resource. Due to the problem of data availability, and ARDL approach is employed to estimate the empirical models.
Using the production function approach, the results of the study confirm that the overall long run effect of oil abundance on GDP is positive and significant but the value of the estimated coefficient is too small. The findings show that physical capital and human capital have positive and significant effects on GDP in the long run. Moreover, this study finds that oil abundance have negative and significant effect on income distribution. It means that oil revenue improves income equality in Iran. It should be point out that while the Gini coefficient is relatively higher compared to most countries, poverty level are substantially lower because of the distinguished social welfare system in the country and cohesive system of private social responsibility through a religious charitable system. However, income and human capital have a negative and significant effect on income distribution. The overall findings appeared to support this hypothesis that oil abundance is not a blessing for Iran.
Seyed Nezamuddin Makiyan, Samaneh Khatami,
Volume 11, Issue 3 (10-2011)
Abstract
The convergence process and the advantages involved for less developed and developing countries, especially those located in the MENA region is of a great importance in economic studies. Through expanding regional co-operations and playing a wider role in the economies of the member states, it can prepare a suitable ground for growing regional markets and positive international economic reactions and finally can result into total development of the region. This article, using time series model is aiming at testing the convergence hypothesis in MENA region (15 countries) during 1980-2008. For analyzing time series model, we used Augmented Dicky Fuller test, Zivot & Andrews (with the endogenous time break) unit root test, Im, Pesaran & Shin and also Levin, Lin & Chu unit root panel data tests. The results of time series model with ADF and ZA tests show that there are two groups of convergence among the selected MENA countries. The first one is those countries which are converging from the low per capita income up to the average per capita income of the selected countries. The second one is the countries which are converging from the high per capita income down to the average of the region. The rest have diverged from the average per capita income during the period. According to Im, Pesaran & Shin and also Levin, Lin & Chu unit root tests, the convergence hypothesis of per capita income to average, is accepted for the whole sample. Altogether, the selected countries are minimizing the gap between their per capita income and the average per capita income of the region.
Firouz Fallahi, Behzad Salmani, Simin Kiani,
Volume 12, Issue 4 (1-2013)
Abstract
This paper examines the existence of β-Convergence between per-capita incomes of selected Islamic countries. For this purpose, data over the period 1965-2006 and a time series approach proposed by Vogelsang (1998) are applied. Robustness of the estimated parameters to the presence of unit roots and/or serial correlations in the residuals is the main advantage of this method. The results show that per-capita income of most countries is converging to the average per-capita income of the selected Islamic countries, which provide evidence of β-Convergence. Cameroon, Indonesia, Malaysia, Niger, Chad, and Togo are the countries that have shown some forms of divergence either before the break date or after that. The estimated break dates are clustered and mostly related to the energy shocks in 1974, 1979, and 1986.
Ahmad Molabahrami, Hassan Khodavaisi, Reza Hossaini,
Volume 13, Issue 1 (4-2013)
Abstract
In this paper, it is tried to propose a robust model for predicting inflation in Iran among alternative models. For doing this, monthly data from April 1990 to the end of September 2009 is used. Firstly, it is tried to determine whether the CPI data is chaotic or stochastic. It is shown that it is chaotic rather than stochastic. Therefore, it is predictable. Then, a stochastic differential equation model is estimated (specifically a geometric Brownian motion) for CPI in Iran. In order to compare the prediction power of the model other alternative models of prediction like ARMA, non-linear GARCH, EGARCH, TGARCH are also used to extrapolate inflation during a six month prediction period. Based on RMSE, MAE, U-Tail, it is revealed that stochastic differential equation model is much more robust than the alternative models mentioned above.
Volume 13, Issue 1 (4-2013)
Abstract
Healthcare providers may need to publish their operational data for consultation as well as to allow more researches. Consequently, a lot of personal specific data with high level of details are publicly available. This data may contain time series, such as ECG. De-identification of time series is not enough to provide the requirement of privacy preservation. It is because, if a few numbers of time series are published, then appearing specific anomalies in them may reveal the sensitive information of an individual. The problem of privacy preserved time series publication is somewhat studied, but the issues of publishing the Ngrams of the time series, especially that of extracted from a small set of time series, are not considered well before. In this paper, we address this problem and define the k-anonymity principle for the Ngram. The proposed schema aims to provide the k-anonymization by repeating the rare n-grams to hide them in the crowd of frequent n-grams. We evaluate our method by using two datasets. Results of experiments show that our method can provide the requested anonymity level with low probability and entropy information loss.
Volume 15, Issue 7 (9-2015)
Abstract
In statistics, Entropy is a measure of disorder of time series. Entropy is used in physiologic for signal analysis. In physiologic science, Entropy is used for performance analysis of body organs such as heart and brain. Epileptic patients have been diagnosed by this technique. In this paper for the first time, Entropy is used to determine the health condition of mechanical systems. A special kind of Entropy, namely Permutation Entropy is used for this purpose.To perform the experiment an apparatus consisting of a motor coupled with a shaft has been designed and manufactured. Vibration signals from supporting bearing of this system in different shaft states namely healthy shaft, and shafts with 3, 5 and 7 mm crack were gathered with a vibration data analyzer. The vibration were taken from sensors mounted on bearing supports of the shaft. Shaft was subjected to a constant bending moment. The vibration signals were preprocessed by permutation Entropy method. Nine different features were extracted from the Entropy signals which are fed to an Adaptive Neuro Fuzzy Inference System (ANFIS). The designed ANFIS was capable of classifying different shaft states with an overall %96 percision.
Volume 20, Issue 4 (10-2018)
Abstract
Horticulture sector plays a prominent role in economic growth for most of the developing countries. India is the largest producer of fruits and vegetables in the world next only to China. Among the horticultural crops, fruit crops are cultivated in majority of the area. Fruit crops play a significant role in the economic development, nutritional security, employment generation, and overall growth of a country. Among fruit crops, mango and banana are largest producing fruits of India. Generally, Karnataka is called as the horticultural state of India. In Karnataka, mango and banana are highest producing fruit crops. With these prospective, yield of mango and banana of Karnataka have been chosen as study variables. Forecasting is a primary aspect of developing economy so that proper planning can be undertaken for sustainable growth of the country. In this study, classes of linear and nonlinear, parametric and non-parametric statistical models have been employed to forecast yield of mango and banana of Karnataka. The major drawback of linear models is the presumed linear form of the model. In most of the cases, the time series are not purely linear or nonlinear as they contain both linear and nonlinear components. To overcome this problem a hybrid model has been proposed which consists of linear and nonlinear models. The hybrid model with the combination of Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression model performed better in both model building as well as in model validation as compared to other models.
Mrs. Azadeh Arab, Dr Ahmad Sarlak, Dr Mojtaba Ghiasi, Dr Maryam Sharifnezhad,
Volume 21, Issue 4 (11-2021)
Abstract
Financial development and stability and their relationship with economic growth is one of the most important and influential issues in economic growth. Therefore, economists have studied this issue by exerting different conditions. The main purpose of this study is to evaluate the impact of financial development and financial stability on Iran's economic growth using the generalized method of moments (GMM) during 1992-2019. The results show positive and meaningful effects of financial development and financial stability on economic growth. The variables of lagged economic growth, education, fixed investment and trade liberalization have positive and significant effects on economic growth, while, inflation, government expenditure and active population have negative impacts on economic growth. Therefore, considering the important role of education, it is recommended to government to invest in this sector and upgrade the production structure, make the necessary structural reforms in the capital market and banking system, direct credit and liquidity to strengthen private sector production, as well as improve the level and composition of government spending in order to increase production efficiency.
Dr Zana Mozaffari, Khaled Ahmadzadeh,
Volume 22, Issue 2 (6-2022)
Abstract
Investing in housing is one of the most common methods of investing in Iran. The housing and construction sector has a widespread relationship with other economic sectors. Housing annually attracts a large amount of liquidity in the country, so investing in this sector is more important in the process of growth. In this paper, first the relationship between investing in housing and economic growth is evaluated by the Granger causality test. The results of this test showed that there is a one-way causal relationship from investing in housing to economic growth. Then, using GMM method and time series data from 1981-2019, the impact of investing in housing on Iran's economic growth is studied. The results showed that investing in housing has a positive and significant effect on Iran's economic growth. The lag of variable “economic growth” has a positive effect on economic growth in later years. Also, other results indicate that human capital, capital stock, government expenditure index and industrialization index have positive and significant effects on Iran's economic growth. Based on the results, it can be suggested that the shortcomings and barriers to investment in the housing sector should be removed, and also incentives and facilities for investment in housing can be effective in increasing economic growth.
Volume 24, Issue 1 (12-2023)
Abstract
An improved method for noise reduction from a time series obtained from a chaotic system is presented. This improved method is based on a noise reduction technique presented by Schreiber and Grassberger that has good performance and less complexity compared to other noise reduction methods from chaotic data. Here a global model created using a neural network has been used as a prediction model for chaotic time series. This global prediction model performs better compared to the local prediction model used in the original method. The improved method also takes advantage of the singular spectrum analysis reconstruction technique. Both of these improvements led to a more accurate noise reduction method while preserving the unique properties of the original. The improved method is applied to a time series obtained from the chaotic state of Lorenz equations that is polluted with Gaussian noise. The final results show a 33 percent reduction in mean absolute error values compared to the original method. Also, the error of calculating the correlation dimension from the data has been reduced to 2 percent after applying the improved method.
Volume 25, Issue 2 (7-2021)
Abstract
Nowadays, given the rapid growth of population, development of infrastructure is inevitable and the pressure of human needs on the soil and exploitation of areas around cities in rural areas are increasing. Access to surface water, fertile soil, groundwater, access to transit roads, etc. have made establishing of new cities compulsory despite the environmental hazards in those areas.
Land deformation as an environmental hazard may be related to tectonic activities such as earthquakes, faults, volcanoes, landslides and anthropogenic processes such as groundwater exploitation, which threaten urban areas. Land surface subsidence is recognized as a potential problem in many areas. This phenomenon is most often caused by human activities, mainly from the removal of subsurface water. Also, Iran with rough and mostly mountainous topography, have a high potential for landslides and instability of slopes.
Pardis new city in the east part of Tehran is one of the areas most prone to Domain Instabilities. The location of the city and its expansion toward the steep slopes have made it susceptible to all kinds of natural hazards, so the main purpose of the study is investigate the potential of landslide and subsidence in Pardis.
Material and Methods
This research consists of two stages: first, ground surface deformation was estimated using radar interferometry technique. Then, landslide susceptible zoning was carried out using Fuzzy and AHP methods.
We applied SBAS algorithm to the 27 SAR images of the Sentinel-1 satellite, in ascending orbit for the time period of 2016.01.06.-2018.12.21. The first step of the SBAS procedure involves the selection of the SAR data pairs to generate the interferograms; the selected images are characterized by a small temporal and spatial separation (baseline) between the orbits in order to limit the noise effect usually referred to as decorrelation phenomena. The second step of the procedure involves the retrieval of the original (unwrapped) phase signals from the modulo-2 π restricted (wrapped) phases directly computed from the interferograms.
In the next stage, landslide susceptibility zones have been evaluated using both fuzzy logic and analytical hierarchy process (AHP) models, as a weighting technique to explore landslide susceptibility mapping. In the modelling process, eight causative variables including aspect, slope degree, altitude, distance from the road, distance from the fault, distance from the river, lithology and land use were identified for landslide susceptibility mapping.
In fuzzy logic the degree of membership of variables may be any real number from 0 (non-membership) to 1 (full membership) which reflects a degree of membership (
Zadeh, 1965). By contrast, in
Boolean logic, the truth values of variables may only be the integer values 0 or 1. After Fuzzification of all layers, since the causative factors are not the same value, the AHP method to determine the weights was performed. The AHP methodology consists of pairwise comparison of all possible pairs of factors. The relative rating for the dominance between each pair of factors was guided by expert knowledge. After obtaining weight of each factors, these weights are multiplied in the map calculated by fuzzy membership.
Results and Discussion
We used 27 c-band sentinel-1 images for the 2016-2018 period and the Small BAseline Subset (SBAS) approach to investigate land deformation in Pardis. Result of the deformation map of Pardis show that the northern part is uplifted with an annual rate of 25 mm/yr. The uplift of the northern part can be attributed to tectonic factors and the southern part of the basin subsided with an annual rate of -35 mm/yr. Thereafter landslide susceptibility areas have been evaluated. Geomorphological variables (slope, aspect, elevation, river), geology variables (lithology, fault) and anthropogenic variables (land use, roads) have been used for generation of the landslide susceptible map. The results of the landslide susceptible map indicate that the northern part of the Pardis basin have a high potential for landslides. Landslide susceptible map is classified into five classes: very high, high, medium, low and very low.
Medium to very high susceptible class covered 40% of the study area which overlay on uplifting areas resulting from radar technique.
Conclusion
SBAS time series method has been used to detect ground surface deformation and vertical movements. This method is based on an appropriate combination of multi look DInSAR Interferograms. Deformation map indicate that northern part of the basin, uplifted and southern part subsided. The cities of Pardis, Roodehen and Boomehen in the southern part, subsided a mean rate of respectively -35, -31 and -29 mm/year. The northern part uplifted with a mean rate of 25 mm/year which can be attributed to tectonic activity. Then, the landslide susceptibility map was created using both Fuzzy and AHP methods. The result show that more than 40% of the basin is exposed to landslides. The results of both methods SBAS time series analysis, landslide susceptibility mapping, demonstrated domain instabilities in northern part of the basin. As a result, identifying instable areas seems necessary for the urban development of the Pardis.
Key words: Pardis city, SBAS time series analysis, landslide, subsidence
