The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Development and Performance Analysis of Fireworks Algorithm-Trained Artificial Neural Network (FWANN): A Case Study on Financial Time Series Forecasting
Abstract
Financial time series are highly nonlinear and their movement is quite unpredictable. Artificial neural networks (ANN) have ample applications in financial forecasting. Performance of ANN models mainly depends upon its training. Though gradient descent-based methods are common for ANN training, they have several limitations. Fireworks algorithm (FWA) is a recently developed metaheuristic inspired from the phenomenon of fireworks explosion at night, which poses characteristics such as faster convergence, parallelism, and finding the global optima. This chapter intends to develop a hybrid model comprising FWA and ANN (FWANN) used to forecast closing prices series, exchange series, and crude oil prices time series. The appropriateness of FWANN is compared with models such as PSO-based ANN, GA-based ANN, DE-based ANN, and MLP model trained similarly. Four performance metrics, MAPE, NMSE, ARV, and R2, are considered as the barometer for evaluation. Performance analysis is carried out to show the suitability and superiority of FWANN.
Related Content
|
Arunaben Prahladbhai Gurjar, Shitalben Bhagubhai Patel.
© 2022.
30 pages.
|
|
Meghna Babubhai Patel, Jagruti N. Patel, Upasana M. Bhilota.
© 2022.
10 pages.
|
|
Vo Ngoc Phu, Vo Thi Ngoc Tran.
© 2022.
27 pages.
|
|
Steven Walczak.
© 2022.
17 pages.
|
|
Priyanka P. Patel, Amit R. Thakkar.
© 2022.
26 pages.
|
|
Vo Ngoc Phu, Vo Thi Ngoc Tran.
© 2022.
34 pages.
|
|
Sarat Chandra Nayak, Subhranginee Das, Bijan Bihari Misra.
© 2022.
20 pages.
|
|
|