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An Introduction to Time-Series Prediction
2017This chapter provides an introduction to time-series prediction. It begins with a formal definition of time-series and gradually explores possible hindrances in predicting a time-series. These hindrances add uncertainty in time-series prediction. To cope up with uncertainty management, the chapter examines the scope of fuzzy sets and logic in the ...
Amit Konar, Diptendu Bhattacharya
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THE CLUSNET ALGORITHM AND TIME SERIES PREDICTION
International Journal of Neural Systems, 1993This paper describes a novel neural network architecture named ClusNet. This network is designed to study the trade-offs between the simplicity of instance-based methods and the accuracy of the more computational intensive learning methods. The features that make this network different from existing learning algorithms are outlined.
Loke-Soo Hsu +2 more
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Prediction of Multivariate Time Series
Journal of Applied Meteorology, 1964Abstract A method of linear prediction of stationary multivariate time series is discussed from the point of view of meteorological applications. Tests of significance are given and it is shown by examples that the method is practical even when the dimensionality of the series becomes quite large.
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2013
This article deals with a smart time series prediction based on characteristic patterns recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history for the purpose of prediction of subsequent trader’s action. The pattern recognition approach is based on neural networks.
Eva Volna +3 more
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This article deals with a smart time series prediction based on characteristic patterns recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history for the purpose of prediction of subsequent trader’s action. The pattern recognition approach is based on neural networks.
Eva Volna +3 more
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Local prediction of chaotic time series
Proceedings of the 33rd Midwest Symposium on Circuits and Systems, 2002Consideration is given to two different methods for chaotic signal local forecasting: a local interpolation and functional reconstruction. A brief review is presented of the mathematical framework on nonstatistical forecasting. An outline of some main techniques for chaotic signal characterization is furnished. >
Giona M +3 more
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Industrial Time Series Prediction
2018Time series prediction is a significant way for forecasting the variables involved in industrial process, which usually identifies the latent rules hidden behind the time series data of the variables by means of auto-regression. In this chapter we introduce the phase space reconstruction technique, which aims to construct the training dataset for ...
Jun Zhao, Chunyang Sheng, Wei Wang
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The adaptive prediction of time series
Fifth Symposium on Adaptive Processes, 1966Adaptive technique determination of optimum operations for pure prediction of discrete time series with respect to mean square error cost ...
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Optimizations in time series clustering and prediction
Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies, 2010In this paper a combination of time series clustering and prediction is considered. Both clustering and prediction are done by neural networks with supervised and unsupervised learning respectively. Some optimizations of the clustering procedure are proposed for software implementation.
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Predicting the Turning Points of a Time Series
The Journal of Business, 1979Students of economic time series recognize the notion of a "turning point" -a point in time when a series which had been increasing reverses and, for a time, decreases.' An example of a turning point is given in figure 1 where the time series of quarterly, seasonally adjusted real GNP is shown.
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Predicting the Direction of a Time Series
2004This chapter proposes and analyzes a new method for predicting the direction of a timeseries, that is, the relative position of future observations with respect to past coordinates, a problem of obvious interest to financial forecasters. The method involves two steps: an embedding step from real-valued observations to discrete values and a prediction ...
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