**1. Introduction**

Observing data points over time with proper transform may reveal valuable information about systems behaviours and trends. This book entitled "Time Series Analysis (TSA) and Applications" comes at a very opportune period where business enterprises are overloaded with data and looking for swift analytics and on the other hand have not yet trusted the powerful algorithms such as deep learning and AI. Academics prefer simple tools like Matlab or Mathematica to run TSA. However, statistics and probabilistic instruments have gained wide acceptance for decades. Time Series Analysis had been often assimilated to finance and forecasting. The chapters presented here prove the contrary and show how far TSA is being applied across an array of disciplines and how efficient and effective this technique could be if it is fittingly utilised. In the same spirit, this chapter provides an overview of time series as applied to detect anomalies in Internet of Things (IoT) networks. Specific attention is paid to anomalies that occur in smart cities IoT use cases. The final aim of this research work partly described here is to mount plug n play anomaly detection engine (ADE).

The Internet has evolved from its original aim of providing access to web resources globally to what is commonly called today Internet of Things, where it is expected that objects will internetwork and have a presence on the Internet just with an IPv6 address for example. The objects market is estimated in billions and trillions, very far from the global human population. This has led to new business models with development of dedicated IoT networks such as SigFox, LoRa, Symphony Link, and NB-IoT, and production of IoT compliant devices from microcontrollers' manufacturers such as Microchip, Intel, and Raspberry PI. Software companies have come up with virtual machines and statistical tools for big data analytics whereas network devices constructors like Cisco and Juniper for instance have come up with network

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

**Figure 1.** IoT Value Chain.

gateways and routers to accommodate devices connection, routing, and IoT data transit. The myriad of technologies involved within the IoT ecosystem should empower smart environments as it happens likewise in smart cities. The next section introduces the IoT value chain and then lists some use cases of IoT in smart environments whereby anomalies arouse, followed by the classification of anomalies in the time domain, the time series models applicable and finally problematics in applying TSA to anomaly detection in IoT.
