Time Series Analysis of data is a big deal in the IoT realm

The combination of IoT with developments in Artificial Intelligence and Augmented Reality is bringing forth a new realm. Concurrently Blockchain technology is supporting IoT by addressing security and privacy issues of IoT. 

Most of the companies started realizing the full potential of IoT with AI enabling machines to make well-informed decisions with little or no human intervention. Augmented Reality started facilitating activities such as prognostic maintenance of devices by generating computer-aided images based on signals emitted by IoT when carrying out maintenance. Blockchain is getting used to ensure the data transmitted through IoT is secured. 

In coming days IoT will be main data provider and provide a mechanism for players in non-digital domains to collect and analyse customer data. As per DBS, IoT is achieving mainstream adoption with a ~14% global consumer adoption rate. With growing uptake, by 2030, ~125bn2 devices are expected to be connected to the internet, at which point DBS estimated that global adoption of consumer of IoT technology will reach ~100%. 

For instance, leading car manufacturing giant #Toyota already partnered with #Hitachi and implemented smart manufacturing in Toyota’s #model plants which is using advanced data analytics from sensors entrenched in manufacturing equipment and #AI to draw insights through predictive analytics and real-time monitoring.

Among all Industries #Energy & Utilities and Manufacturing sectors are leading in #IoT adoption.

#Logistics and transportation #Industry also started realizing that with thousands of different types and forms of goods being stored in the warehouse today, every square metre of warehousing space must be optimally utilised to ensure specific goods can be retrieved, processed, and delivered as fast as possible. Use of #delivery drones and #robots to conduct deliveries will become a reality now with the advancement of the IoT.

On the other side when we are talking about smart city, smart parking provides real-time parking information to enable better parking management. Huawei is working on a smart parking project with China Unicom (Shanghai), which expects to connect tens of millions of devices with this smart parking service.

Few years back Duke Energy had deployed an asset health monitoring and alerting system involving over 30,000 IoT sensors25. It allowed the company to move from a semi-annual, manual, and paper-based asset #inspection and reporting #system to a fully #automated, #real-time system. In the three years of its operation, advanced analytics on the sensor data has helped the company avoid over US$31mn in maintenance costs alone.

According to Cisco’s annual Visual #Networking Index, machine-to-machine (M2M) connections that support IoT applications will account for more than half of the world’s 27.1 billion devices and connections by 2021. According to IDC, worldwide technology spending on the Internet of Things to reach $1.2T in 2022, attaining a compound annual growth rate (CAGR) of 13.6% by 2022. Ericcson is forecasting the number of cellular IoT connections is expected to reach 3.5B in 2023, increasing at a CAGR of 30%.

With all these industrial revolution, the value added services to IoT and key differentiator is the Data Analytics, part which comprises the anomaly detection component with the help of Time Series Analysis(#TSA).

In coming years Data analytics in IoT will be a highest income generator than key technology enablers.

Big #software companies have arisen up with virtual machines and statistical tools for big data analytics whereas network devices manufacturer like #Cisco and #Juniper for instance had come up with network gateways and routers to accommodate devices connection, routing, and IoT data transit.

Time series data is a big deal in the IoT. A time series is a series of data points collected at regular intervals and indexed in time order – the sort of reading you might see, for example, from a smart electricity meter in a home or from meteorology kit for forecasting the weather.

In my knowledge, #InfluxData is one of many data analytics players, largely focusing on time series data analysis. It’s open source software enables developers to build monitoring, analytics and IoT applications. #AWS IoT Analytics is also a fully-managed service that makes it easy to run and operationalize analytics on IoT data without having to worry about the cost and complexity typically required to #build an IoT analytics platform.

So, let us understand what Time Series Analysis is on a 300 feet level just to appreciate the fact why Time Series Analysis is so meaningfully used with IoT. A complete discussion on TSA is beyond the scope and objective of this article and I may cover a #full-fledged Time Series Analysis in some other article.

As per #Wikipedia, Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.

WHAT THIS MEANS?

A time series is a collection of observations of well-defined data items obtained through repeated measurements over time. For example, measuring the value of retail sales each month of the year would comprise a time series. This is because sales revenue is well defined, and consistently measured at equally spaced intervals. Data collected irregularly or only once are not time series.

An observed time series can be decomposed into three components:

Trend: Gradual shift or movement to relatively higher or lower values over a long period of time. When the time series analysis shows a general pattern, that is upward, we call it uptrend. When the trend pattern exhibits a general pattern, that is down we call it a downtrend. If there were no trend, we call it horizontal trend or stationary trend

#Seasonality: Upward or downward swings. Repeating pattern within a fixed time period

#Cyclical Patterns: Repeating up and down movements. Usually go over more than a year of time. Don't have a fixed period. Much harder to predict

#Irregular: Erratic, unsystematic, 'residual' fluctuations. Mostly happens due to random variation or unforeseen events

#White Noise: Describes the assumption that each element in a series is a random draw from a population

Mostly two basic types of TSA models are used 1) #ARIMA, which relate the present value of a series to past values and past prediction errors and 2) Ordinary #Regression Models, which is helpful for an initial description of the data and form the basis of several simple forecasting methods

Process used in TSA prediction are 1)Visualize the time series, 2) #Stationalize the series, 3) Plot #ACF/#PACF chart and find optimal parameters, 4) build ARIMA model and 5) make prediction

We are already witnessing that Time Series Forecasting and Internet of Things (IoT) has got momentum, now it’s time to think how to utilize this opportunity in coming days.

#Time_Series_Analysis #Augmented #Reality #Artificial_Intelligence #AI #IoT #Blockchain #Data #Analytics #differentiator #virtual #machines #statistical #tools #big #data #analytics#AI#ML