Hybrid Model Using Interacted-ARIMA andANN Models forEfficient Forecasting

Title

Hybrid Model Using Interacted-ARIMA andANN Models forEfficient Forecasting

Creator

Baskaran T.; John N.; Dhandra B.V.

Description

When two models applied to the same dataset produce two different sets of forecasts, it is a good practice to combine the forecasts rather than using the better one and discarding the other. Alternatively, the models can also be combined to have a hybrid model to obtain better forecasts than the individual forecasts. In this paper, an efficient hybrid model with interacted ARIMA (INTARIMA) and ANN models is proposed for forecasting. Whenever interactions among the lagged variables exist, the INTARIMA model performs better than the traditional ARIMA model. This is validated through simulation studies. The proposed hybrid model combines forecasts obtained through the INTARIMA model from the dataset, and those through the ANN model from the residuals of INTARIMA, and produces better forecasts than the individual models. The quality of the forecasts is evaluated using three error metrics viz., Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Empirical results from the application of the proposed model on the real dataset - lynx - suggest that the proposed hybrid model gives superior forecasts than either of the individual models when applied separately. The methodology is replicable to any dataset having interactions among the lagged variables.. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Source

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol-14078 LNAI, pp. 747-756.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

INTARIMA; Interacted lagged variables; Interaction; Time series

Coverage

Baskaran T., CHRIST (Deemed to be University), Hosur Road, Karnataka, Bengaluru, 560029, India; John N., CHRIST (Deemed to be University), Hosur Road, Karnataka, Bengaluru, 560029, India; Dhandra B.V., CHRIST (Deemed to be University), Hosur Road, Karnataka, Bengaluru, 560029, India

Rights

Restricted Access

Relation

ISSN: 3029743; ISBN: 978-303136401-3

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-031-36402-0_69

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164954546&doi=10.1007%2f978-3-031-36402-0_69&partnerID=40&md5=b298399f8f7e51b3f4a2b4cfd11b4d6f