Next Values: Time Series Prediction . Forecasting the Future and Understanding the Past. Andreas S. Weigend and Neil A. Gershenfeld, Eds. Addison-Wesley, Reading, MA, 1993. xx, 643 pp., illus. $49.50; paper, $32.25. Santa Fe Institute Studies in the Sciences of Complexity, vol. 15. From a workshop, Santa Fe, NM, May 1992.

Science ◽  
1994 ◽  
Vol 265 (5179) ◽  
pp. 1745-1746
Author(s):  
Andrew R. Solow
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Santa Fe ◽  
The Past ◽  
The Future ◽  
Sciences Of Complexity

scholarly journals Incomplete Time Series Prediction Using Max-Margin Classification of Data with Absent Features

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Shang Zhaowei ◽  
Zhang Lingfeng ◽  
Ma Shangjun ◽  
Fang Bin ◽  
Zhang Taiping
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Prediction ◽  
Forecast Model ◽  
Imputation Method ◽  
Experimental Results ◽  
Optimal Hyperplane ◽  
The Future ◽  
Margin Classification

This paper discusses the prediction of time series with missing data. A novel forecast model is proposed based on max-margin classification of data with absent features. The issue of modeling incomplete time series is considered as classification of data with absent features. We employ the optimal hyperplane of classification to predict the future values. Compared with traditional predicting process of incomplete time series, our method solves the problem directly rather than fills the missing data in advance. In addition, we introduce an imputation method to estimate the missing data in the history series. Experimental results validate the effectiveness of our model in both prediction and imputation.

scholarly journals Disaster Forecasting Approach In Indonesia: A Fuzzy Time Series - Markov Chain Model

2019 ◽  
Author(s):  
Rahmad Syah
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Natural Disasters ◽  
Markov Chain Model ◽  
Volcanic Eruptions ◽  
Fuzzy Time Series ◽  
Chain Model ◽  
The Past ◽  
The Future ◽  
Transport Accidents

The concept of Fuzzy Time Series to predict things that will happen based on the data in the past, while Markov Chain assist in estimating the changes that may occur in the future. With methods are used to predict the incidence of natural disasters in the future. From the research that has been done, it appears the change, an increase of each disaster, like a tornado reaches 3%, floods reaches 16%, landslides reaches 7%, transport accidents reached 25% and volcanic eruptions as high as 50%.

Prediction of Financial Time-Series Signals Using á Trous Wavelet Transform

2015 ◽  
Vol 781 ◽  
pp. 523-526 ◽  
Author(s):  
Wassanun Sangjun ◽  
Supawat Supakwong ◽  
Suttipong Thajchayapong
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Polynomial Regression ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Real World Data ◽  
Financial Time ◽  
The Past ◽  
Significant Performance

This paper proposes a financial time-series prediction method consisting of á Trous wavelet transform and polynomial regression. The main purpose of employing á Trous wavelet transform is to decompose financial time-series signals into different resolutions where only relevant signal components are used for prediction. Also, á Trous wavelet transform is used to avoid the edge problem where only the past and present components of the time-series signal are taken into account. The decomposed time-series signals are then fed into the polynomial regression part to obtain predicted time-series signals. Using real-world data, performance evaluation is conducted based on total benefit and profit/loss where it is shown that á Trous wavelet transform contributes to a significant performance improvement.

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