A Neural Network Method for Nonlinear Time Series Analysis

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Jinu Lee
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Nonlinear Time Series ◽  
Power Performance ◽  
Forward Selection ◽  
Neural Network Models ◽  
Neural Network Method ◽  
Series Analysis ◽  
Network Method

Abstract This paper is concerned with approximating nonlinear time series by an artificial neural network based on radial basis functions. A new data-driven modelling strategy is suggested for the adaptive framework by combining the statistical techniques of forward selection, cross validation and information criterion. The proposed method is fast and simple to implement while avoiding some typical difficulties such as estimation and computation of nonlinear econometric models. Two applications are provided to illustrate the benefits of using the neural network method in time series analysis. First, the proposed modelling method is applied to a neural network test for neglected nonlinearity in conditional mean of univariate time series. A simulation study is carried out to show how the size of the test is improved in finite samples. Further, the new test is compared with alternative popular tests to demonstrate its superior power performance using a variety of nonlinear time series models. Second, the proposed method is applied to obtain a nonlinear forecasting model for daily S&P 500 returns. Forecast accuracy is compared with that of a linear model and other neural network models used in the literature.

scholarly journals Forecasting variance of NiftyIT index with RNN and DNN

2022 ◽  
Vol 2161 (1) ◽  
pp. 012005
Author(s):  
C R Karthik ◽  
Raghunandan ◽  
B Ashwath Rao ◽  
N V Subba Reddy
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Network Models ◽  
Training Data ◽  
Complex Data ◽  
Neural Network Models ◽  
Series Analysis

Abstract A time series is an order of observations engaged serially in time. The prime objective of time series analysis is to build mathematical models that provide reasonable descriptions from training data. The goal of time series analysis is to forecast the forthcoming values of a series based on the history of the same series. Forecasting of stock markets is a thought-provoking problem because of the number of possible variables as well as volatile noise that may contribute to the prices of the stock. However, the capability to analyze stock market leanings could be vital to investors, traders and researchers, hence has been of continued interest. Plentiful arithmetical and machine learning practices have been discovered for stock analysis and forecasting/prediction. In this paper, we perform a comparative study on two very capable artificial neural network models i) Deep Neural Network (DNN) and ii) Long Short-Term Memory (LSTM) a type of recurrent neural network (RNN) in predicting the daily variance of NIFTYIT in BSE (Bombay Stock Exchange) and NSE (National Stock Exchange) markets. DNN was chosen due to its capability to handle complex data with substantial performance and better generalization without being saturated. LSTM model was decided, as it contains intermediary memory which can hold the historic patterns and occurrence of the next prediction depends on the values that preceded it. With both networks, measures were taken to reduce overfitting. Daily predictions of the NIFTYIT index were made to test the generalizability of the models. Both networks performed well at making daily predictions, and both generalized admirably to make daily predictions of the NiftyIT data. The LSTM-RNN outpaced the DNN in terms of forecasting and thus, grips more potential for making longer-term estimates.

Iterated neural network models for time series analysis

METRON ◽  
2011 ◽  
Vol 69 (2) ◽  
pp. 129-149 ◽  
Author(s):  
Zouaoui Chikr-El-Mezouar ◽  
Mahmoud Mohamed Hassan Gabr
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Network Models ◽  
Neural Network Models ◽  
Series Analysis

Nonlinear Time Series Analysis with R

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Particle Physics ◽  
Linear Time ◽  
Nonlinear Time Series ◽  
Nonlinear Time Series Analysis ◽  
Series Analysis ◽  
Linear Behavior ◽  
Non Linear ◽  
Scientific Principles

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjects of science, such as mathematical topology, relativity or particle physics. For this reason, the tools of NLTS have been confined and utilized mostly in the fields of mathematics and physics. However, many natural phenomena investigated I many fields have been revealing deterministic non linear structures. In this book we aim at presenting the theory and the empirical of NLTS to a broader audience, to make this very powerful area of science available to many scientific areas. This book targets students and professionals in physics, engineering, biology, agriculture, economy and social sciences as a textbook in Nonlinear Time Series Analysis (NLTS) using the R computer language.

Sign in / Sign up

Export Citation Format

Share Document