Centre and Range method for fitting a linear regression model to symbolic interval data

“…To settle on the issue, we consider the four stock indices consisting of candle chart as a one data point, and propose a candle chart-valued time series by aggregating information of four stock indices at time t to as an one data point. We also propose approaches for fitting a time series model to symbolic candle chart-valued time series data based on midpoints and half ranges of open and close indices, and of highest and lowest indices, in line with the Centre and Range method (CRM method) for intervalvalued data (Lima Neto and De Carvalho, 2008). The forecasted candle chart allows us to predict the rise or fall of the stock index.…”

Section: Introduced Various Forecasting Methods For a Histogram Time mentioning

confidence: 99%

“…Billard and Diday (2000) introduced linear regression modeling approaches to symbolic interval-valued data based on the midpoint of data intervals. To improve the model's prediction performance, Lima Neto and De Carvalho (2008) proposed a new approach based on information about the midpoint and half range of the intervals. Lima Neto et al (2006) also proposed novel sum of squares methods, called NCRM1 and NCRM2, which considered the correlation between the midpoint and half range.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Symbolic Candle Chart-Valued Time Series

Park¹,

Sakaori²

2014

Communications for Statistical Applications and Methods

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This study introduces a new type of symbolic data, a candle chart-valued time series. We aggregate four stock indices (i.e., open, close, highest and lowest) as a one data point to summarize a huge amount of data. In other words, we consider a candle chart, which is constructed by open, close, highest and lowest stock indices, as a type of symbolic data for a long period. The proposed candle chart-valued time series effectively summarize and visualize a huge data set of stock indices to easily understand a change in stock indices. We also propose novel approaches for the candle chart-valued time series modeling based on a combination of two midpoints and two half ranges between the highest and the lowest indices, and between the open and the close indices. Furthermore, we propose three types of sum of square for estimation of the candle chart valued-time series model. The proposed methods take into account of information from not only ordinary data, but also from interval of object, and thus can effectively perform for time series modeling (e.g., forecasting future stock index). To evaluate the proposed methods, we describe real data analysis consisting of the stock market indices of five major Asian countries'. We can see thorough the results that the proposed approaches outperform for forecasting future stock indices compared with classical data analysis.

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“…In some methods, intervalvalued observations are replaced by precise values based on some additional assumptions, for example, by taking middle points of intervals (Lima Neto & de Carvalho, 2008). This approach can be successfully used when intervals are not large and the area produced by the interval intersections is rather small (see, for example, the left picture in Fig.…”

Section: Introductionmentioning

confidence: 99%