Time series data prediction is used in several applications in the area of science and engineering. Time series prediction models have been implemented using statistical approaches, but recently, neural networks are being applied for times series prediction due to their inherent properties and capabilities. A variation of a standard neural network called as finite impulse response (FIR) neural ...

We consider statistical aspects of the modelling and prediction theory of time series in one and many dimensions. We discuss Lévy-based and general models, and the stationary and non-stationary cases. Our starting point is the recent pair of surveys, Szegö’s theorem and its probabilistic descendants and Multivariate prediction and matrix Szegö theory, by this author.

We derive solutions for the problem of missing and noisy data in nonlinear time-series prediction from a probabilistic point of view. We discuss diierent approximations to the solutions, in particular approximations which require either stochastic simulation or the substitution of a single estimate for the missing data. We show experimentally that commonly used heuristics can lead to suboptimal...

This paper introduces an application of the Sequential Network Construction ( snc) method to select the size of several popular neural network predictor architectures for various benchmark training sets. The specific architectures considered are a fir network and the partially recurrent Elman network and its extension, with context units also added for the output layer. We consider an enhanceme...

Time series prediction covers a vast field of every-day statistical applications in medical, environmental and economic domains. In this paper we develop nonparametric prediction strategies based on the combination of a set of “experts” and show the universal consistency of these strategies under a minimum of conditions. We perform an indepth analysis of real-world data sets and show that these...

With the increasing collection of time series data, both related to business and personal use, a substantial amount of research and development efforts are being directed to gain deeper insights from such records. Data mining techniques like similarity search and segmentation are used as tools to enhance the comprehension of this data. While innovative techniques have been examined, less work h...

The diÆculty of predicting time series generated by chaotic systems has motivated the development of many time series prediction algorithms. Among these local models have emerged as one of the most accurate methods. A weakness of local models is their sensitivity to the choice of user selected parameters such as the size of the neighborhood, the embedding dimension, and the distance metric. Thi...

Using sequence transformation and extrapolation algorithms for the prediction of further sequence elements from a finite number of known sequence elements is a topic of growing importance in applied mathematics. For a short introduction, see the book of Brezinski and Redivo Zaglia 1, Section 6.8 . We mention theoretical work on prediction properties of Padé approximants and related algorithms l...

In this paper we address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on the noise terms. Using regret minimization techniques, we develop effective online learning algorithms for the prediction problem, without assuming that the noise terms are Gaussian, identically distributed or even independent. Furthermore, we show ...

In this project machine learning techniques were used to generate technical trading strategies in the US interest rate swap markets. Leela, a correlation algorithm that is closely related to autoregression, was developed to detect short term repeating patterns to predict future market moves. This algorithm exhibited a Sharpe Ratio of 1 when applied to a single swap. When additional swaps were u...