scholarly journals Context Based Predictive Information

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 645
Author(s):  
Yuval Shalev ◽  
Irad Ben-Gal
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Lossy Compression ◽  
Compression Algorithm ◽  
Predictive Information ◽  
Real Dataset ◽  
Information Bottleneck ◽  
The U.S

We propose a new algorithm called the context-based predictive information (CBPI) for estimating the predictive information (PI) between time series, by utilizing a lossy compression algorithm. The advantage of this approach over existing methods resides in the case of sparse predictive information (SPI) conditions, where the ratio between the number of informative sequences to uninformative sequences is small. It is shown that the CBPI achieves a better PI estimation than benchmark methods by ignoring uninformative sequences while improving explainability by identifying the informative sequences. We also provide an implementation of the CBPI algorithm on a real dataset of large banks’ stock prices in the U.S. In the last part of this paper, we show how the CBPI algorithm is related to the well-known information bottleneck in its deterministic version.

A time-series analysis of the U.S. kidney transplantation and the waiting list: donor substitution effects

2010 ◽  
Vol 42 (1) ◽  
pp. 261-277 ◽  
Author(s):  
T. Randolph Beard ◽  
John D. Jackson ◽  
David Kaserman ◽  
Hyeongwoo Kim
Keyword(s):  
Time Series ◽  
Kidney Transplantation ◽  
Time Series Analysis ◽  
Waiting List ◽  
Substitution Effects ◽  
Series Analysis ◽  
The U.S

Determinants of Common Stock Prices: A Time Series Analysis

1977 ◽  
Vol 32 (2) ◽  
pp. 417-425 ◽  
Author(s):  
Marshall R. Blume ◽  
John Kraft ◽  
Arthur Kraft
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Stock Prices ◽  
Common Stock ◽  
Series Analysis

scholarly journals Detecting Determinism in Time Series with Complex Networks Constructed Using a Compression Algorithm

2018 ◽  
Vol 28 (13) ◽  
pp. 1850165
Author(s):  
Débora C. Corrêa ◽  
David M. Walker ◽  
Michael Small
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Information Content ◽  
Compression Algorithm ◽  
Connectivity Structure ◽  
Cycle Basis ◽  
Minimal Cycle ◽  
Time Irreversibility ◽  
Basis Structure

The properties of complex networks derived from applying a compression algorithm to time series subject to symbolic ordinal-based encoding is explored. The information content of compression codewords can be used to detect forbidden symbolic patterns indicative of nonlinear determinism. The connectivity structure of ordinal-based compression networks summarized by their minimal cycle basis structure can also be used in tests for nonlinear determinism, in particular, detection of time irreversibility in a signal.

CONVERGENCE AND THE U.S. STATES: A TIME-SERIES ANALYSIS*

1996 ◽  
Vol 36 (4) ◽  
pp. 597-616 ◽  
Author(s):  
Gerald Carlino ◽  
Leonard Mills
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis ◽  
The U.S

Forecasting Performance Comparison by Using Power Transformation between VAR and Bayesian VAR Models

2014 ◽  
Vol 529 ◽  
pp. 621-624
Author(s):  
Syang Ke Kung ◽  
Chi Hsiu Wang
Keyword(s):  
Time Series ◽  
Industrial Production ◽  
Multivariate Time Series ◽  
Performance Comparison ◽  
Power Transformation ◽  
Var Models ◽  
Bayesian Var ◽  
Forecasting Performance ◽  
The U.S ◽  
Forecasting Practice

This article is devoted to examine the performance of power transformation in VAR and Bayesian VAR (BVAR) forecasts, in comparison with log-transformation. The effect of power transformation in multivariate time series model forecasts is still untouched in the literature. We examined the U.S. macroeconomic data from 1960 to 1987 and the Taiwan’s technology industrial production from 1990 to 2000. Our results showed that the power transformation provides outperforming forecasts in both VAR and BVAR models. Moreover, the non-informative prior BAVR with power transformation is the best predictive model and is recommendable to forecasting practice.

scholarly journals Predicting Stock Market Index Using Hybrid Intelligence Model

2018 ◽  
Vol 7 (3.15) ◽  
pp. 36 ◽  
Author(s):  
Sarah Nadirah Mohd Johari ◽  
Fairuz Husna Muhamad Farid ◽  
Nur Afifah Enara Binti Nasrudin ◽  
Nur Sarah Liyana Bistamam ◽  
Nur Syamira Syamimi Muhammad Shuhaili
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Linear Models ◽  
Forecast Error ◽  
Arima Model ◽  
Real Data ◽  
Stock Index ◽  
Support Vector ◽  
Ann Model ◽  
Stock Market Index

Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.  

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