Prediction of selected Indian stock using a partitioning–interpolation based ARIMA–GARCH model

C. Narendra Babu · B. Eswara Reddy

70 citations0/0 files storedApplied Computing and Informatics

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

Accurate long-term prediction of time series data (TSD) is a very useful research challenge in diversified fields. As financial TSD are highly volatile, multi-step prediction of financial TSD is a major research problem in TSD mining. The two challenges encountered are, maintaining high prediction accuracy and preserving the data trend across the forecast horizon. The linear traditional models such as autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedastic (GARCH) preserve data trend to some extent, at the cost of prediction accuracy. Non-linear models like ANN maintain prediction accuracy by sacrificing data trend. In this paper, a linear hybrid model, which maintains prediction accuracy while preserving data trend, is proposed. A quantitative reasoning analysis justifying the accuracy of proposed model is also presented. A moving-average (MA) filter based pre-processing, partitioning and interpolation (PI) technique are incorporated by the proposed model. Some existing models and the proposed model are applied on selected NSE India stock market data. Performance results show that for multi-step ahead prediction, the proposed model outperforms the others in terms of both prediction accuracy and preserving data trend.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 70 citations exist upstream.

References

1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 2003Time series forecasting using a hybrid ARIMA and neural network model 1992ARCH modeling in finance 2001Principles of forecasting : a handbook for researchers and practitioners 1994Chapter 49 Arch models 2010A novel hybridization of artificial neural networks and ARIMA models for time series forecasting 2004Risk and Volatility: Econometric Models and Financial Practice 2002Principles of Forecasting. A Handbook for Researchers and Practitioners

Cited by

2021Multi-step time series analysis and forecasting strategy using ARIMA and evolutionary algorithms 2019Analysis of temporal pattern, causal interaction and predictive modeling of financial markets using nonlinear dynamics, econometric models and machine learning algorithms 2022Research on Stock Price Time Series Prediction Based on Deep Learning and Autoregressive Integrated Moving Average 2023Machine learning techniques for stock price prediction and graphic signal recognition 2018

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.