Analysis and Forecasting of Financial Time Series Using R. Models and Applications

      Аннотация: Analysis and prediction of stock market time series data have attracted considerable interest from the research community over the last decade. Rapid development and evolution of sophisticated algorithms for statistical analysis of time series data and availability of high-performance hardware have made it possible to process and analyze high volume stock market time series data effectively, in real-time. Among many other important characteristics and behavior of such data, forecasting is an area which has witnessed considerable focus. This book presents some of the state of the art research work in the field of time series analysis and forecasting. Rich libraries of R software have been used for time series decomposition and for designing of efficient forecasting approaches. It will surely be a valuable source of knowledge for researchers, engineers, practitioners, analysts, data scientists and graduate and doctoral students who are working in the field of econometrics, statistical modeling, time series analysis, forecasting and financial analytics. It will also be useful for faculty members of graduate schools and universities.

Ключевые слова: ARIMA, Decomposition, Mutual Fund, Neural Network, seasonal, Trend, Time Series, Random, Holt Winters Forecasting model, R Programming Language, Auto Sector Index, Small Cap Sector Index, Consumer Durable Sector Index, IT Sector Index, Capital Goods Sector Index, Dow Jones Industrial Average (DJIA) Index, NIFTY Index, Foreign Exchange, Heath Care Sector Index, FMCG Sector Index, Linear Regression, Root Mean Square Error.