주식시장 내부에 있는 구조들과 무작위성 분석

학위논문(석사) -- 서울대학교대학원 : 자연과학대학 수리과학부, 2021.8. Otto van Koert.이 논문에서는 주식 시장 분석에 있어서 이론적 방법과 데이터 분석 방법을 사용한 다. 실제 시장이 무작위하게 움직이는지에 대한 질문을 던지며 논문을 시작한다. Mapper와 Autoencoder를 이용하여 주식시장의 구조를 비교해본다.In this paper, we give a theoretical approach and an approach with data analysis for stock market. We start with a direct question which is ‘Do the stock market always follow the random walk?’. Then we will see structures of time series in stock market by comparing mapper with autoencoder.1 Test of Randomness 4 1.1 Preliminaries 4 1.2 Theoretical Approach for Stochastic Process 5 1.3 Wiener Process 5 1.4 Kolmogorov-Smirnov Test 7 1.5 Shapiro-Wilk Test 8 1.6 Validation for test of randomness 10 1.7 Check the Seasonality 10 2 SARIMA Model 11 2.1 Preliminaries 11 2.2 Autoregressive(AR) 12 2.3 Moving Average(MA) 12 2.4 Autoregressive Integrated Moving Average(ARIMA) 13 2.5 Seasonal Autoregressive Integrated Moving Average(SARIMA) 13 2.6 Forecast the Stock price 14 2.7 Summary 15 3 Events in Randomness 16 3.1 Preliminaries 16 3.2 Poisson Process with jump diffusion 17 3.3 Poisson Distribution 20 3.4 Distribution of Traded Price 21 3.5 Conclusion 23 4 Classify Time Series 24 4.1 Preliminaries 24 4.2 Hellinger distance 25 4.3 Mapper 27 4.4 Autoencoder 28 4.5 Visualization 29석

An Automatic ReRAM SPICE Model Generation Methodology for ReRAM-Circuit Co-Simulation

MasterThis paper presents an automatic ReRAM SPICE model generation methodology which enables true ReRAM-circuit co-simulation in standard SPICE. In our method, a model generation tool automatically produces SPICE models of various ReRAM devices and selectors from the measured I-V data including newly measured ReRAM's SET behavior in our proposed experiment. The generated models are compatible with standard SPICE and can describe diverse ReRAM behaviors including nonlinear I-V characteristic, I-V relationship during SET, and dependency of I-V curve on SET current. Because describing these behaviors in standard SPICE has been the critical obstacle to simulate ReRAMs and circuits together, especially in multi-level cell simulation, our method can be a key enabler of ReRAM-circuit co-simulation. To verify our method, SPICE models for diverse ReRAMs were generated and simulated. The results show that our model can accurately describe the original data. To demonstrate the usefulness of our method, we also simulated and analyzed example ReRAM circuits such as 1R and 1S1R arrays and a complex CMOS sensing circuit for ReRAM. These simulations and analyses show that our method enables quantitative ReRAM analyses such as trade-off analyses for reading margin and sensing delay

Read Margin Analysis in an ReRAM Crossbar Array

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