Data Forecasting

An Industry Brief

What is Data Forecasting

Forecasting uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. Choosing appropriate forecasting techniques depends on the type of data being used and behavior of the data. For example, forecasting techniques used for “financial data” may not be appropriate for time series data like “computer server data” or “data center network activity data” (though underlying forecasting principles stay the same). Continue Reading Data Forecasting

Sightline has expertise in collecting, retaining & analyzing time-series data.

What is “Time-Series Data”?

Time series data is a collection of quantities that are assembled over even intervals in time and ordered chronologically. The statistical characteristics of time series data often violate the assumptions of conventional statistical methods. Because of this, analyzing time-series data requires a unique set of tools and methods, collectively known as time series analysis.

Forecasting is one of the ways to analyze time series to find out future trends of the given input data.

There Are Two Classes of Popular Data Forecasting Techniques

Univariate Time-Series: Only one variable is varying over time. For example, data collected from a sensor measuring the speed of a motor every second. Therefore, each second, you will only have a one-dimensional value, which is the speed.

Multivariate Time-Series: Multiple variables are varying over time. For example, a tri-axial accelerometer. There are three accelerations, one for each axis (x,y,z) and they vary simultaneously over time.

There are multiple forecasting methods or formulas available in each technique. GARCH, SARIMA etc., are examples of Univariate technique. VAR, VECM etc., are examples of multivariate techniques.

How to Pick The Appropriate Forecasting Model

Apart from univariate vs multivariate techniques, the structure (or) behaviour of data is what influences in picking appropriate “model” or “forecast formula”. Few things which impact behaviour of data are: seasonality, trend (linear or nonlinear) etc., in the data.

For example, while using univariate data, SARIMA with appropriate parameters could be a good model to use while dealing with time series data with seasonality. If no seasonality doesn’t exist in the data, ARIMA might be a good approach.

SARIMA: It refers to Seasonal ARIMA.It is basically for series with seasonality component in them which hasn’t been handled in any way. Here we need to provide 7 parameters where 3 for ARIMA(AR, I, MA) and 3 for Seasonal ARIMA(Season AR, Season I, Season MA) and one for seasonality duration(12 months,6 months according to the data)

ARIMA: It is AutoRegression Integrated MovingAverage. Here Integrated term refers to differencing that is, for example, calculating 5th term with order 3, it would be 4th + (4th-3rd)+(3rd-2nd) terms.

Sightline EDM analytic capabilities processes the time series data to help customers in identifying future trends of their machine data by using above described techniques. The advantage with Sightline EDM is, it automatically goes through the data to identify seasonality/stationarity of data and applies appropriate forecasting methods to identify future trends.

Histogram Data

A histogram is used to summarize discrete or continuous data, grouping data points into specified range values, called “bins.” The histogram is similar to a vertical bar graph; however, the histogram shows no space between the bars. Creating a histogram provides a visual representation of data distribution, and have the ability to displace a large amount of data and the frequency of data values. The median distribution of the data can be determined by a histogram, as well as showing any outliers or gaps in the data. Continue Reading Histogram Data

MQTT Protocol

MQTT (Message Queueing Telemetry Transport) is a simple, lightweight message publishing and subscribing network protocol. It is the standard protocol for Internet of Things (IoT) and Industry 4.0 messaging.

It is lightweight, low bandwidth, and functions well in high latency and unreliable environments, making it ideal in production environments. Devices send data (publish) to an MQTT Broker with a topic and a data payload, and devices can subscribe to that topic and subtopics and receive updates containing that data from the broker when the data changes. Topics can be defined to use several levels of depth and devices can subscribe to topics using wildcards allowing for dynamic changes when required. Continue Reading MQTT Protocol

Zero-Trust Data Security

Zero-Trust is a security concept and cybersecurity framework allowing an organization to aggressively defend itself, its data, and user permissions using an advanced system of automated security protocols. Zero-Trust is centered on the belief that organizations should not automatically trust anything inside or outside their perimeters and instead must verify anything and everything trying to connect to their systems before granting access. Continue Reading Zero-Trust Data Security

Data Correlation

The EDM Correlation data analysis tool is designed to help users perform root cause analysis across multiple devices or systems. It compares the activity of one system over a given period with the activity of multiple other systems and identifies the most highly related data points. This allows EDM to create an intuitive visualization of related events across an entire network of mixed technology enabling root cause analysis to be performed more efficiently than ever before. Continue Reading Data Correlation