Relevant bibliographies by topics / Simple moving average

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Simple moving average'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 June 2025

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Journal articles on the topic "Simple moving average"

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Pradipbhai, Naik Parth. "Comparison Between Exponential Moving Average Based MACD with Simple Moving Average Based MACD of Technical Analysis." International Journal of Scientific Research 2, no. 12 (2012): 189–97. http://dx.doi.org/10.15373/22778179/dec2013/60.

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Widodo, David, and Seng Hansun. "Implementasi Simple Moving Average dan Exponential Moving Average dalam Menentukan Tren Harga Saham Perusahaan." Jurnal ULTIMATICS 7, no. 2 (2016): 113–24. http://dx.doi.org/10.31937/ti.v7i2.354.

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The rapid economic growth, technology and science have a big impact on human mind for searching and getting money, one of the example is stocks investment. The information is so easy to find and disseminated, as well as the awareness and knowledge are the other key to be able to know and understand how to be a good investor. The purpose of this research was to implement simple moving average and exponential moving average to determine the trend of the stock price of a company. The application is by calculating the value of a simple moving average and exponential moving average period of 5 (weekly) and a period of 20 (monthly) then analyze the results of calculation with the data on the fact whether it is appropriate or not. The tests for this research is conducted using data from the 15 companies listed in the Indonesia Stock Exchange (IDX) and always included in the list of LQ45 index from 2005 to 2014. From the research that began on January 6, 2014 until September 29, 2015 for the simple moving average and the exponential moving average period of 5 (weekly) can be inferred that the level of accuracy of the simple moving average 5-day period was at 61,73%, then to the result of the exponential moving average 5-day period was at 68,07%. Then, based on research results that starting on January 1, 2014 until September 29, 2015 for the simple moving average and the exponential moving average 20day period (monthly) can be inferred that the level of accuracy of the simple moving average 20-day period was at 72,52%, and then to result 20-day period exponential moving average is at 73,39%.
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Ren, L., and P. Ren. "Type I error of t-tests from the simple moving average technical trading rules." Applied Econometrics 61 (2021): 47–61. http://dx.doi.org/10.22394/1993-7601-2021-61-47-61.

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Grigg, Olivia, and David Spiegelhalter. "A Simple Risk-Adjusted Exponentially Weighted Moving Average." Journal of the American Statistical Association 102, no. 477 (2007): 140–52. http://dx.doi.org/10.1198/016214506000001121.

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GALBRAITH, J. W., and V. ZINDE-WALSH. "A simple noniterative estimator for moving average models." Biometrika 81, no. 1 (1994): 143–55. http://dx.doi.org/10.1093/biomet/81.1.143.

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Suratman, Suratman. "Expert Advisor Foreign Exchange Menggunakan Simple Moving Average." Jurnal Bangkit Indonesia 7, no. 1 (2018): 30. http://dx.doi.org/10.52771/bangkitindonesia.v7i1.33.

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Foreign exchange (forex) trading adalah transaksi perdagangan nilai tukar mata uang asing di pasar uang internasional, pada saat ini perdagangan foreign exchange banyak diminati oleh masyrakat luas. Pada dasarnya trading forex (foreign exchange) dilakukan secara manual, sehingga hal itu membutuhkan pemantauan grafik harga secara terus-menerus pada layar monitor dan tentu saja itu membuang waktu dan tenaga serta psikologi trader sendiri. Berdasarkan permasalahan yang ada, penulis melakukan analisa dan merancang expert advisor menggunakan indikator simple moving average yang mengantisipasi kelemahan-kelemahan pada trading manual.Perancangan expertadvisor menggunakan indikator simple moving average yang penulis usulkan telah menjawab permasalahan yang ada. Dengan demikian expert advisor ini akan memberikan hasil yang baik dalam trading. Software yang digunakan sebagai alat bantu dalam penelitian ini adalah menggunakan metaeditor dan metatrader 4, metodologi yang digunakan untuk pengembangan expert advisor ini adalah menggunakan metode sekuensial linier.
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Fatimah, Fajar, Andi Tejawati, and Novianti Puspitasari. "Prediksi Pemakaian Air PDAM Menggunakan Metode Simple Moving Average." Jurnal Rekayasa Teknologi Informasi (JURTI) 2, no. 1 (2018): 55. http://dx.doi.org/10.30872/jurti.v2i1.1410.

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Penelitian ini bertujuan untuk membangun sebuah sistem yang mampu memprediksi pemakaian air di PDAM di kota Samarinda wilayah Bengkuring.dengan metode Simple Moving Average. Pengambilan data dalam penelitian ini dilakukan dengan observasi secara langsung dan wawancara dengan bagian distribusi PDAM Titra Kencana Samarinda. Penelitian ini menggunakan data pemakaian air di PDAM mulai dari bulan september 2016- januari 2017 untuk memprediksi pemakaian air bulan dibulan berikutnya. Dalam penelitian ini digunakan nilai MAPE untuk menghitung nilai error atau ketidak tepatan hasil peramalan sehingga hasil prediksi akan mendekati sempurna. Sistem yang dibagun dalam penelitian ini menggunakan bahasa PHP dengan koneksi database MYSQL.dimana rumus perhitungan prediksi dimasukkan ke dalam sistem. Hasil dari sistem yang dibangun yaitu jumlah pemakaian air disetiap bulan dengan nilai MAPE 0,1712 termasuk kriteria ‘sangat baik’ sehingga dapat disimpulkan sistem ini dapat digunakan untuk memprediksi pemakaian air PDAM Tirta Kencana Samarinda.
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Baskara, Gama Paksi, Suyanto Suyanto, and Sri Retnaning Rahayu. "PENGARUH VOLUME PERDAGANGAN DAN SIMPLE MOVING AVERAGE TERHADAP HARGA SAHAM." Jurnal Akuntansi AKTIVA 1, no. 1 (2020): 1–16. http://dx.doi.org/10.24127/akuntansi.v1i1.41.

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Trading volume is a sheet of company shares traded on a particular transaction and has beenagreed between the seller and the buyer, Simple Moving Average is a method that studies themovement of the previous stock price based on the number of certain days in order to predict thestock price that will occur to the next.The objective of the study is to find out how much influenceTrade Volume and Simple Moving Average on Stock Prices is and what are the most dominantaspects in influencing Stock Prices. The type of the research uses a quantitative approach, namely anapproach in which the data are in the form of numbers or qualitative data that have been used asnumbers. The technique of collecting data uses documentation. The analytical tool used is multiplelinear regression tests including T Test, F Test and Coefisein R² Determination processed usingEviews. The results of the study show that partially the trading volume variable does not have asignificant effect on Stock Prices and the Simple Moving Average variable shows a positive andsignificant effect on stock prices while the results of the research simultaneously show that theTrading Volume and Simple Moving Average variables simultaneously affect the Stock Price .
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Vaidya, Rashesh. "Accuracy of Moving Average Forecasting for NEPSE." Journal of Nepalese Business Studies 13, no. 1 (2020): 62–76. http://dx.doi.org/10.3126/jnbs.v13i1.34706.

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A simple moving average is one of the oldest and the simplest techniques of forecasting the trends of the stock market. The technical analysts follow mainly three types of moving averages, namely; simple, weighted, and exponential moving averages. Among these three types, as per the interest of investors, short-term and long-term time duration is used to calculate the trend using the moving average. All the mentioned moving averages are used by investors or analysts to predict the future trends of the market using historical data. Hence, for evaluating their forecasting accuracy, the paper has used both the short-term and the long-term moving average. The paper has used the NEPSE (closing) index values to calculate as well as plotted the moving averages to forecast the future trend and its accuracy with the help of Mean Absolute Percentage Error (MAPE). The paper found that there is a better crossover in the graphical representation of the moving average in the long-term moving average. In context to the Nepalese stock market, the MAPE results reflected a weekly (5-trading days) 5-SMA analysis of the market movement as the most relevant in short-term forecasting. Similarly, using the technique of moving average, 200-SMA (200-trading days of a year) was seen as the most effective to forecast long-term trends. The result of the long-term moving average MAPE pointed out that the annual reports of the listed companies better determine the trend of the market.
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Harmsen, Samuel M., Yu-Hui H. Chang, and Steven J. Hattrup. "Simple Moving Average: A Method of Reporting Evolving Complication Rates." Orthopedics 39, no. 5 (2016): e869-e876. http://dx.doi.org/10.3928/01477447-20160517-02.

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Dissertations / Theses on the topic "Simple moving average"

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Chan, Fong Kun. "The profitability of simple moving average trading rules in the Hong Kong stock market." Thesis, University of Macau, 2001. http://umaclib3.umac.mo/record=b1636208.

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Cucurnia, Renato, and Ali Khadar. "Value at Risk : En kvantitativ studie av Historical Simulation Approach och Simple Moving Average Approach." Thesis, Umeå University, Umeå School of Business, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-34300.

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Šimkutė, Jovita. "Akcijų kainų kintamumo analizė." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2007. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2007~D_20070816_142218-96053.

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Darbe „Akcijų kainų kintamumo analizė“ nagrinėjami ir lyginami Baltijos (Lietuvos, Latvijos, Estijos) bei Lotynų Amerikos (Meksikos, Venesuelos) šalių duomenys. Atliekama pasirinktų akcijų kainų grąžų analizė. Jai naudojami trijų metų kiekvienos dienos duomenys (akcijų kainos). Pirmoje darbo dalyje supažindinama su bendra prognozavimo metodų teorija, aprašomi skirtingi, dažnai literatūroje ir praktikoje sutinkami modeliai. Antrojoje dalyje aprašyti prognozavimo metodai taikomi realiems duomenims, t.y. pasirinktoms akcijoms. Prognozuojama akcijų kainų grąža, kuri po to yra palyginama su realia reikšme, apskaičiuojamos prognozavimo metodų paklaidos. Pagrindinis darbo tikslas – atlikti lyginamąją prognozavimo modelių analizę su pasirinktomis akcijomis ir atrinkti tuos metodus, kurie duoda geriausius rezultatus. Darbo tikslui įgyvendinti naudojama SAS statistinio paketo ekonometrikos ir laiko eilučių analizės posistemė SAS/ETS (Time Series Forecasting System).<br>Most of empirical surveys in macro and financial economics are based on time series analysis. In this work, data of Baltic and Latin America countries is being analyzed and compared. Analysis of stock price returns is presented using daily long term (three years) period data. In the first part of this work general forecasting theory is presented, also different methods, frequently met in the literature and practice, are described. In the second part, forecasting models are being applied for real data. We present results of forecasting stock returns comparing them with real values. Also a precision of forecasts is being calculated, which let us to decide about propriety of each model. Consequently, the aim of this work is to forecast returns of stock price by various time series models and to choose the best one. The analysis was made using SAS statistical package and its econometrics and Time Series Analysis System (SAS/ETS).
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李杰恩. "Volatility risks in carry trade - an application of simple moving average method." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/842mzy.

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Soenyoto, Felly Liliyana, and Felly Liliyana Soenyoto. "Trading Performance of Simple Moving Average and Stochastic Oscillator in Indonesia Stock Market." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/46ggak.

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碩士<br>國立臺灣科技大學<br>財務金融研究所<br>106<br>ABSTRACT As Indonesia’s economy continues to grow, the profitability of stock market has become increasingly attractive for the many of investors. This research aims to investigate the usefulness of two commonly used technical analysis, which are simple moving average and stochastic oscillator in providing excess return to the large-cap stocks in Indonesia stock market. Statistic tests are used as tools to measure the performances of both technical indicators based on 30 samples of large-cap stocks in Indonesia for the period of 2007-2017. To avoid the undesirable effect of market’s net trend on the back-test result, the performance is measured during a sub period in which the data has no trend. The result shows that both simple moving average and stochastic oscillator do not provide a statistically significant excess return on large-cap stocks in Indonesia. Also, no significant difference is found between the return provided by both trading rules. The results support the Efficient Market Hypothesis (EMH) that is if the market is efficient, then any trading rule will not provide excess return. Besides, it is also found that a high winning probability does not necessarily guarantee that the trading rule can generate a profitable return as well. Keyword: Technical analysis, Simple Moving Average, Stochastic Oscillator, Indonesia
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Chien, Po-Yen, and 簡伯諺. "Technical Trading Strategy with Weighted Moving Average and Simple Slope Indicator for the Textile Stock in Taiwan." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/97026654492680952559.

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碩士<br>逢甲大學<br>紡織工程所<br>98<br>This study aims to analyze one certain stock via computer-assisted program written in programming by Microsoft Visual Studio 2008. Using Weighted Moving Average and Simple Slope Indicator as technical analysis; moreover, taking the variability of trading volume into consideration, the research discusses the profit rate of the two technical index. The research makes the information of stock price go under the training and testing of Back Propagation Networks (BPN). Finally, observe if the anticipated stock price trend is better than the one of the other two technical indexes. The result showed the Weighted Moving Averages of various days show that a stable and high profit is guaranteed. Unlike the Simple Slope Indicator of different days shows the lack of stability; however, it is more likely to bring profit that is above quota. Back Propagation Networks, probably due to not taking all kinds of conditions into consideration while testing the prediction sample, the trend dropped in a dramatic pace, which shows the deficiency of accuracy.
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Royston, Guy Andrew. "Assessing a quantitative approach to tactical asset allocation." Diss., 2012. http://hdl.handle.net/2263/27023.

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The purpose of this paper is to determine whether the adoption of a simple trend-following quantitative method improves the risk-adjusted returns across various asset classes within a South African market setting. A simple moving average timing model is tested since 1925 on the South African equity and bond markets and within a tactical asset allocation framework. The timing solution when applied to the JSE All Share Index, RSA Government Bond Index and within an equally weighted portfolio improved returns, while reducing risk. Testing the model within sample by decade highlighted periods of inferior return performance providing evidence to support prior research (Faber, 2007) that the timing model acts as a risk reduction technique with limited to no impact on return.<br>Dissertation (MBA)--University of Pretoria, 2012.<br>Gordon Institute of Business Science (GIBS)<br>unrestricted
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CHANG, YEN-CHIANG, and 張彥強. "The Apply of Wavelet Analysis to Improve the Return by the Simple Moving Average of Technical Analysis - Based on Taiwan’s Security Market." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/v587s4.

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碩士<br>國立中山大學<br>企業管理學系研究所<br>102<br>In this study, The author choose Taiwan stock market as the research market, mainly discuss about whether can use the function of well denoising of wavelet analysis in investment securities. Due to the problem of fake trading signal by using technical analysis, therefore, the investor who use technical analysis can easily make a wrong diagnosis about the deal timing of long strategy and short strategy and also loss. By using the function of denoising of wavelet analysis, that will eliminate the high frequency signal (white noise) which is a fake signal. To improve the accuracy of the deal timing of long strategy and short strategy by investors who are using technical analysis. In this study, the research is based on long strategy and short strategy by simple moving average. After having confirmed that use wavelet analysis can significantly improve investment performance, further use taguchi method to find out the number of days of optimal short-term or medium-term’s simple moving average which is under Taiwan&apos;&apos;s stock market, to make the maximize of return in practice. （1）Use wavelet analysis can significantly improve investment performance which is based on long strategy and short strategy by simple moving average. （2）Use taguchi method to find out the number of days of optimal short-term or medium-term’s simple moving average. （3）Use statistics can find the interval of Taiwan weighted index that can achieve the most return on investment. （4）In the framework of this study into investment methods, ETF’s （Exchange Traded Funds）overall investment performance is better than other individual stocks.
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Books on the topic "Simple moving average"

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McCleary, Richard, David McDowall, and Bradley J. Bartos. Intervention Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0005.

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The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.
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Book chapters on the topic "Simple moving average"

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Kohrangi, M., A. N. Papadopoulos, S. R. Kotha, D. Vamvatsikos, and P. Bazzurro. "Earthquake Catastrophe Risk Modeling, Application to the Insurance Industry: Unknowns and Possible Sources of Bias in Pricing." In Springer Tracts in Civil Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68813-4_11.

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AbstractMathematical risk assessment models based on empirical data and supported by the principles of physics and engineering have been used in the insurance industry for more than three decades to support informed decisions for a wide variety of purposes, including insurance and reinsurance pricing. To supplement scarce data from historical events, these models provide loss estimates caused to portfolios of structures by simulated but realistic scenarios of future events with estimated annual rates of occurrence. The reliability of these estimates has evolved steadily from those based on the rather simplistic and, in many aspects, semi-deterministic approaches adopted in the very early days to those of the more recent models underpinned by a larger wealth of data and fully probabilistic methodologies. Despite the unquestionable progress, several modeling decisions and techniques still routinely adopted in commercial models warrant more careful scrutiny because of their potential to cause biased results. In this chapter we will address two such cases that pertain to the risk assessment for earthquakes. With the help of some illustrative but simple applications we will first motivate our concerns with the current state of practice in modeling earthquake occurrence and building vulnerability for portfolio risk assessment. We will then provide recommendations for moving towards a more comprehensive, and arguably superior, approach to earthquake risk modeling that capitalizes on the progress recently made in risk assessment of single buildings. In addition to these two upgrades, which in our opinion are ready for implementation in commercial models, we will also describe an enhancement in ground motion prediction that will certainly be considered in the models of tomorrow but is not yet ready for primetime. These changes are implemented in example applications that highlight their importance for portfolio risk assessment. Special consideration will be given to the potential bias in the Average Annual Loss estimates, which constitutes the foundation of insurance and reinsurance policies’ pricing, that may result from the application of the traditional approaches.
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"Moving Average Convergence/Divergence (MACD)." In 12 Simple Technical Indicators. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119204428.ch4.

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"Moving Averages." In 12 Simple Technical Indicators. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119204428.ch2.

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Tliche, Youssef, Atour Taghipour, and Béatrice Canel-Depitre. "Exploring a Downstream Demand Inference Strategy in a Decentralized Two-Level Supply Chain." In Advances in Logistics, Operations, and Management Science. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3805-0.ch001.

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A coordination approach for forecast operations, known as downstream demand inference, enables an upstream actor to infer the demand information at his formal downstream actor without the need for information sharing. This approach was validated if the downstream actor uses the simple moving average (SMA) forecasting method. To answer an investigative question through other forecasting methods, the authors use the weighted moving average (WMA) method, whose weights are determined in this work thanks to the Newton's optimization of the upstream average inventory level. Starting from a two-level supply chain, the simulation results confirm the ability of the approach to reduce the mean squared error and the average inventory level, compared to a decentralized approach. However, the bullwhip effect is only improved after a certain threshold of the parameter of the forecasting method. Still within the framework of the investigation, they carry out a comparison study between the adoption of the SMA method and the WMA method. Finally, they generalize their results for a multi-level supply chain.
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Tliche, Youssef, Atour Taghipour, and Béatrice Canel-Depitre. "Anticipation of Demand in Supply Chains." In Hierarchical Planning and Information Sharing Techniques in Supply Chain Management. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7299-2.ch001.

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The main objective of studying decentralized supply chains is to demonstrate that a better interfirm collaboration can lead to a better overall performance of the system. Many researchers studied a phenomenon called downstream demand inference (DDI), which presents an effective demand management strategy to deal with forecast problems. DDI allows the upstream actor to infer the demand received by the downstream one without information sharing. Recent study showed that DDI is possible with simple moving average (SMA) forecast method and was verified especially for an autoregressive AR(1) demand process. This chapter extends the strategy's results by developing mean squared error and average inventory level expressions for causal invertible ARMA(p,q) demand under DDI strategy, no information sharing (NIS), and forecast information sharing (FIS) strategies. The authors analyze the sensibility of the performance metrics in respect with lead-time, SMA, and ARMA(p,q) parameters, and compare DDI results with the NIS and FIS strategies' results.
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Kerimov, Alexander K., and Oleg I. Pavlov. "Dynamic Risk Management of Investment Portfolio by Futures Contracts." In Regaining Global Stability After the Financial Crisis. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4026-7.ch008.

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This chapter is devoted to the dynamic risk management of the investment portfolio using future contracts. The number of futures for each portfolio asset, which is determined by portfolio effectiveness and acceptability of risk at each step, serves as a control parameter. The authors define effective portfolios as the ones of the minimum variance with the expected return greater than or equal to the specified value. Risk is measured by the probability of losing a certain part of the portfolio value. Effective adaptive strategies of portfolio risk management are proposed and their comparative analysis is carried out on a concrete example. In order to determine risk management strategies, the authors implement simple methods of volatility forecasting and correlation of relative changes of price data based on exponential moving average.
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Evangelos, Vasileiou. "Is Technical Analysis Profitable even for an Amateur Investor?" In Handbook of Research on Behavioral Finance and Investment Strategies. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7484-4.ch015.

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The purpose of this chapter is to examine if even the simplest trading rules could take advantage of the market's inefficiency and lead to profitable trading decisions. For this reason, this study examined the profitability of the simplest trading rules, using only the simple moving averages (SMA) rules that even an amateur investor could apply. In order to examine the specific issue a data sample from the Greek stock market during the period 2002-12 was used. The results suggest that even if one takes into account the most expensive transaction fees, the trading rules signal profitable investment decisions; therefore, even an amateur trader and/or investor who does not have a significant amount of money to invest (which may lead to reduced transaction costs) could take advantage of the market's inefficiency. Behavioral finance theories may provide some useful and alternative explanations regarding some of the reasons that contribute to the Greek stock market's inefficient environment.
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Furbish, David Jon. "Fluids and Porous Media as Continua." In Fluid Physics in Geology. Oxford University Press, 1997. http://dx.doi.org/10.1093/oso/9780195077018.003.0006.

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Let us anticipate that we wish to treat fluids as continuous substances at a microscopic scale. To see the motivation for this, it is instructive to consider the possibility of describing fluid behavior at a molecular scale by making use of Lagrangian mechanics to track the behavior of each molecule, just as we would describe the ballistics of a moving, rigid body. Consider describing the state of a simple diatomic molecule at some instant; to do this, we must decide what minimum set of coordinates completely specifies the position and configuration of the molecule. For example, we must specify its position within an inertial reference frame, which requires the three Cartesian coordinates x, y, and z. We also must specify its velocity with respect to this coordinate system, which requires the three corresponding components of velocity um, vm, and wm. The molecule may be spinning; to describe this, we must assign to the molecule three local coordinate axes to specify three angular coordinates that give its orientation within the inertial reference frame. Because the axis of rotation may not coincide with one of the local axes, we also must specify two angular coordinates that give the orientation of the axis of rotation within the local coordinate system. Finally, we must specify the angular velocity about this axis of rotation. Thus, in addition to specifying the mass of a molecule, we need twelve variables or generalized coordinates to specify its state. Moreover, we must know initial values of these twelve coordinates, just as we need to know the initial position and velocity of a ballistic body to track its course. To track the behavior of N molecules, we therefore must know N masses plus 12N initial position, velocity, and orientation coordinates. It becomes clear that to adopt this approach to describe the behavior of a fluid constitutes a formidable task! One has no choice but to abandon a molecular scale treatment and adopt a view involving the microscopic scale, where behaviors of individual molecules are ignored, and instead, the collective behavior of a suitably defined ensemble of molecules is treated in a statistical (average) sense in terms of bulk properties such as fluid density, temperature, and viscosity.
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Pearson, Ronald K. "Linear Dynamic Models." In Discrete-time Dynamic Models. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195121988.003.0004.

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It was emphasized in Chapter 1 that low-order, linear time-invariant models provide the foundation for much intuition about dynamic phenomena in the real world. This chapter provides a brief review of the characteristics and behavior of linear models, beginning with these simple cases and then progressing to more complex examples where this intuition no longer holds: infinite-dimensional and time-varying linear models. In continuous time, infinite-dimensional linear models arise naturally from linear partial differential equations whereas in discrete time, infinite-dimensional linear models may be used to represent a variety of “slow decay” effects. Time-varying linear models are also extremely flexible: In the continuous-time case, many of the ordinary differential equations defining special functions (e.g., the equations defining Bessel functions) may be viewed as time-varying linear models; in the discrete case, the gamma function arises naturally as the solution of a time-varying difference equation. Sec. 2.1 gives a brief discussion of low-order, time-invariant linear dynamic models, using second-order examples to illustrate both the “typical” and “less typical” behavior that is possible for these models. One of the most powerful results of linear system theory is that any time-invariant linear dynamic system may be represented as either a moving average (i.e., convolution-type) model or an autoregressive one. Sec. 2.2 presents a short review of these ideas, which will serve to establish both notation and a certain amount of useful intuition for the discussion of NARMAX models presented in Chapter 4. Sec. 2.3 then briefly considers the problem of characterizing linear models, introducing four standard input sequences that are typical of those used in linear model characterization. These standard sequences are then used in subsequent chapters to illustrate differences between nonlinear model behavior and linear model behavior. Sec. 2.4 provides a brief introduction to infinite-dimensional linear systems, including both continuous-time and discrete-time examples. Sec. 2.5 provides a similar introduction to the subject of time-varying linear systems, emphasizing the flexibility of this class. Finally, Sec. 2.6 briefly considers the nature of linearity, presenting some results that may be used to define useful classes of nonlinear models.
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Kaya, Onur, and Sennur Ulukus. "Power Allocation for Cooperative Communications." In Cooperative Communications for Improved Wireless Network Transmission. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-665-5.ch003.

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In this chapter, we review the optimal power allocation policies for fading channels in single user and multiple access scenarios. We provide some background on cooperative communications, starting with the relay channel, and moving onto mutually cooperative systems. Then, we consider power control and user cooperation jointly, and for a fading Gaussian multiple access channel (MAC) with user cooperation, we present a channel adaptive encoding policy, which relies on block Markov superposition coding. We obtain the power allocation policies that maximize the average rates achievable by block Markov coding, subject to average power constraints. The optimal policies result in a coding scheme that is simpler than the one for a general multiple access channel with generalized feedback. This simpler coding scheme also leads to the possibility of formulating an otherwise non-concave optimization problem as a concave one. Using the perfect channel state information (CSI) available at the transmitters to adapt the powers, we demonstrate significant gains over the achievable rates for existing cooperative systems. We consider both backwards and window decoding, and show that, window decoding, which incurs less decoding delay, achieves the same sum rate as backwards decoding, when the powers are optimized.
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Conference papers on the topic "Simple moving average"

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Hui, Xiao-feng, and Ya-jun Wu. "Research on simple moving average trading system based on SVM." In 2012 International Conference on Management Science and Engineering (ICMSE). IEEE, 2012. http://dx.doi.org/10.1109/icmse.2012.6414356.

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Lauren, Stefan, and S. Dra Harlili. "Stock trend prediction using simple moving average supported by news classification." In 2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA). IEEE, 2014. http://dx.doi.org/10.1109/icaicta.2014.7005929.

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Serheiev-Horchynskyi, Oleksii. "Analysis of Frequency Characteristics of Simple Moving Average Digital Filtering System." In 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T). IEEE, 2019. http://dx.doi.org/10.1109/picst47496.2019.9061237.

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Imaizumi, Hideaki, Tomohiro Nagata, Goro Kunito, Kenichi Yamazaki, and Hiroyuki Morikawa. "Power saving technique based on simple moving average for multi-channel Ethernet." In 2009 14th OptoElectronics and Communications Conference (OECC). IEEE, 2009. http://dx.doi.org/10.1109/oecc.2009.5213197.

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Imaizumi, Hideaki, Tomohiro Nagata, Goro Kunito, Kenichi Yamazaki, and Hiroyuki Morikawa. "Power Saving Mechanism Based on Simple Moving Average for 802.3ad Link Aggregation." In 2009 IEEE Globecom Workshops. IEEE, 2009. http://dx.doi.org/10.1109/glocomw.2009.5360735.

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Macijauskas, Lukas. "Simple Moving Average as a Risk Management Method in Main Asset Classes." In The 7th International Scientific Conference "Business and Management 2012". Vilnius Gediminas Technical University Publishing House Technika, 2012. http://dx.doi.org/10.3846/bm.2012.016.

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Chanhom, Peerapon, Siriroj Sirisukprasert, and Natchpong Hatti. "A new mitigation strategy for photovoltaic power fluctuation using the hierarchical simple moving average." In 2013 IEEE International Workshop on Intelligent Energy Systems (IWIES). IEEE, 2013. http://dx.doi.org/10.1109/iwies.2013.6698557.

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Chantarakasemchit, Orawan, Siranee Nuchitprasitchai, and Yuenyong Nilsiam. "Forex Rates Prediction on EUR/USD with Simple Moving Average Technique and Financial Factors." In 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2020. http://dx.doi.org/10.1109/ecti-con49241.2020.9157907.

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Sulandari, Winita, and Yudho Yudhanto. "Forecasting trend data using a hybrid simple moving average-weighted fuzzy time series model." In 2015 International Conference on Science in Information Technology (ICSITech). IEEE, 2015. http://dx.doi.org/10.1109/icsitech.2015.7407822.

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Hidayat, Achmad Alfian, Zainal Arief, and Dedid Cahya Happyanto. "Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people." In 2015 International Electronics Symposium (IES). IEEE, 2015. http://dx.doi.org/10.1109/elecsym.2015.7380803.

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