Unexpected Features of Financial Time Series: Higher-Order Anomalies and Predictability

Higher order neural networks for financial time series prediction

Generalized correlation higher order neural network designs are developed. Their performance is compared with that of first order networks, conventional higher order neural network designs, and higher order linear regression networks for financial time series prediction. The correlation higher order neural network design is shown to give the highest accuracy for prediction of stock market share...

Financial Time Series and Their Features

Time series of prices as well as time series based on prices or time series which describe prices and their dynamism are called financial time series. These time series have some typical properties. There are two basic assumptions: normality and linearity of log returns of the financial time series. The distributions of log returns are usually skewed and more peaked that the normal distribution...

Time Series Analysis by Higher Order Crossings

Time Series Predictability

A new metric that quantifies the predictability of a time series is introduced. This new time series predictability metric is developed based on the η -metric method introduced by Kaboudan, but overcomes the resolution and stationarity problems presented in the pure η -metric method. It also provides a new feature, which shows how the predictability changes over different subsequences in a time...

Visualizing Time Series Predictability

Predicting how temporally varying phenomena will evolve over time, or in other terms forecasting, is one of the fundamental tasks in time series analysis. Prediction has gained particular importance with the advent of real time data collection activities. Although there exist several sophisticated methodologies to predict time series, the success of a predictive analysis process remains mostly ...