Journal of High Speed Networks 26 (2020) 209–223 209 DOI 10.3233/JHS-200639 IOS Press Analysis of artiﬁcial neural network performance based on inﬂuencing factors for temperature forecasting applications M. Madhiarasan a,∗ , M. Tipaldi b and P. Siano c a Independent Researcher, No: 14, Uzhaippaali Street, Annai Anjugam Nagar, Ayapakkam, Chennai, Tamil Nadu, Pin Code-600077, India E-mail: mmadhiarasan89@gmail.com; ORCID: https://orcid.org/0000-0003-2552-0400 b Department of Engineering, University of Sannio, 82100 Benevento, Italy E-mail: mtipaldi@unisannio.it; ORCID: https://orcid.org/0000-0003-4003-1613 c Department of Management Innovation Systems, University of Salerno, 84084 Salerno, Italy E-mail: psiano@unisa.it; ORCID: https://orcid.org/0000-0002-0975-0241 Abstract. Artiﬁcial neural network (ANN)-based methods belong to one of the most growing research ﬁelds within the artiﬁcial intelligence ecosystem, and many novel contributions have been developed over the last years. They are applied in many contexts, although some “inﬂu- encing factors” such as the number of neurons, the number of hidden layers, and the learning rate can impact the performance of the resulting artiﬁcial neural network-based applications. This paper provides a deep analysis about artiﬁcial neural network performance based on such factors for real-world temperature forecasting applications. An improved back propagation algorithm for such applications is also presented. By using the results of this paper, researchers and practitioners can analyse the encountered issues when applying ANN-based models for their own speciﬁc applications with the aim of achieving better performance indexes. Keywords: Artiﬁcial neural network, optimization, modeling and simulation, improved back propagation neural network, temperature forecasting applications 1. Introduction In recent years, weather forecasting has been playing a vital role in day-to-day life. Weather warnings are important forecasts because they are used to protect life and property [1]. They can be used for the smart home, load forecasting, renewable energy forecasting, ﬁre hazard prevention, weather report and so on [24]. There is a variety of end users to weather forecasts. For instance, utility companies use them to estimate demand over coming days and improve the reliability, availability, serviceability, and usability of the provided services [2]. Among other things, it is worthwhile mentioning some services provided over communication networks, such as real time communication and multimedia services (VoIP, online games, audio and video streaming), solutions for fully distributed architectures (clouds and grids), evolutionary network services, and storage area networking solutions. Within the weather forecasting scope, temperature forecasting with high accuracy is a challenging task because of the inﬂuence of different atmospheric parameters [23]. Many researchers suggest various temperature forecast- ing models. In this paper, we propose a temperature forecasting model using artiﬁcial neural networks (ANN). * Corresponding author. E-mail: mmadhiarasan89@gmail.com. 0926-6801/20/$35.00 © 2020 – IOS Press and the authors. All rights reserved