As for the case study of stock market analysis, I want to introduce two case studies I did before. The one is using existing traditional time series data analysis approach to detect seasonality, stationary/non-stationary, autocorrelations and so on. The other approach is developed by myself to detect the intra-patterns among the same features of stock, and inter-patterns between different features of stock. Below are details of two approaches respectively. Time Series Data Analysis with ARIMA and ClassifiersThe problem of stock analysis is typically formulated as a problem of predicting stock movements based on daily closing prices collected over a period. To solve the forecasting/predicting problem, both time series modeling approach (e.g. ARIMA model) and machine learning approach (e.g. SVM, Decision Tree, ANN prediction method) could be considered. The aims of the study are to identify a model best fitting the time series of DJA stock price from 2014 to 2018 and to forecast the stock price in 2019. I also provide two discussion on the result by using traditional time series analysis approach, the one is whether the seasonal model with different periods would improve the forecast result, the other is whether different time intervals would improve the forecast result. In addition, I also apply the machine learning approaches to construct classifiers and discuss the differences between the result of time-series modeling and the result of machine learning. Code(R)
Multivariate Time Series Data AnalysisA multivariate time series (MTS) is made up of data collected by monitoring the values of a set of temporarily related or interrelated variables over a period of time at successive instants spaced at uniform time intervals. Given a set of MTS, the problem of classification or clustering such data is concerned with discovering inherent groupings of the data according to how similar or dissimilar the time series are to each other. Related Papers: Basic Ideas: A Model-Based Multivariate Time Series Clustering Algorithm Clustering for Portfolios: An Algorithm for Fuzzy Clustering of Multivariate Time Series Prediction using Temporal Patterns: Discovering Fuzzy Temporal Association in Multivariate Time Series for Stock Analysis Corporate Communication Network and Stock Price Movements |
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2-advportfoliopython.pdf | |
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