Background and Objectives: Iran has always attempted to promote its scientific position in the region and the world, and this has frequently been considered in Iran's outlook and comprehensive scientific plan. Therefore, the country should focus on this end, and according to the current capabilities, has initiated significant efforts and has achieved some success in this regard. Iran's scientific progress has been remarkable in recent years. In 1996, Iran had only 829 articles in the Scopus database, but in 2014, this number reached as high as 40000. Accordingly, Iran's impact on world science has expanded. Reviewing Iranian scientists’ works indexed in scientific citation databases from 1996 to 2014 indicates a significant rise in Iranians’ scientific production.  According to Merat et al. (2009), during recent years, in line with the rapid growth of universities and research centers, the number of scientific articles has grown remarkably. Observing these research articles is very significant for the country's scientific progress (Merat et al., 2009). Each country's scientific production indexed in valid international databases reflects a significant part of that country's scientific activities at the international level. Thus, to assess scientific activities, research administrators of the country have always considered having a clear image of this status. Consequently, using ISI, Scopus, and SCImago databases’ data, the present study aims to review the rate of Iran’s scientific production from 1996 to 2015, and the projection of this trend up to 2030. The present article attempts to answer the following questions:
- What is Iran’s regional and world rank and position in terms of scientific article production over the past two decades (1996-2015)?
 - Given the trend of scientific article production during these years, and also the trend of the past, how will the rate of scientific articles’ production and growth in the upcoming years (up to 2030)?
Methodology: In the present article, Excel and Eviews software are used to analyze the data and to make predictions. In this study, using autoregressive integrated moving average (ARIMA), Iran's scientific production up to 2030 is projected. The research data were gathered from ISI and Scopus databases, as well as the SCImago website, which uses Scopus data to evaluate journals and countries’ scientific ranks. Choosing the section related to these databases’ addresses, the main collection of documents related to Iran's scientific production in various scientific fields, as well as all scientific production from 1996 to 2015 were extracted.
Box and Jenkins have made new tools of estimation, which are technically known as the ARIMA methodology. In these models, only dependent and residual variable interruptions are used. Therefore, ARIMA models are sometimes called non-theoretical models because they are not obtained from economic theories. ARIMA model is a summarized form of vector patterns, and if sufficient data is provided, it can estimate time series as accurately as vector patterns. Unlike econometric models that use economic theories and statistical data, time-series models only use statistical data. The time-series models that only relate the current values of a variable to its past values and the current and past error values are called single-variable time-series. These models include autoregressive processes (AR), moving average process (MA), autoregressive moving average (ARMA), and autoregressive integrated moving average process (ARIMA). The mathematical structure of the ARIMA (p,d,q) model is as follows:
 Where θ are the parameters of the moving average, p is the order of the autoregressive model, q is the order of the moving-average model, d is the degree of differencing, L is the lag operator, {Yt} are the observed values, and α is time-series mean. The string error Ut is assumed to be a random variable with a normal distribution, zero mean, and α2 variance. Generally, modeling stages in time series based on Box-Jenkins repeated trend, include four stages of experimental identification of the model structure, estimation of the unknown parameters of the model, recognition of the model's fitting, and prediction with the selected model.
Findings: Based on the statistics of the valid citation databases, the research findings show that despite fluctuations in Iran's scientific production from 1996 to 2015, overall, the country's position over these years has been favorable and above the global average.

 
Discussion:  Analyzing the data, it is projected that Iran's scientific production will be about 44713 articles at the end of 2030. This means that Iran's scientific production will grow 13% by 2030 compared to 2015, and this is an insignificant growth compared to an increase of approximately 48 times between 1996 and 2015. To improve this trend and to increase the country's scientific production, it is essential to adopt a coherent plan and a long-term policy to maintain the country's scientific acceleration and achieve a favorable position in the world.