Data Forecasting

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.

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