Time Series Analysis

In this series I am going to provide you very brief introduction about time series analysis.

Introduction

  • Time play a very important role in success of any business, here time series helps us to see ahead of time.
  • Time series is a sequence of equally distributed data point in order of time. Data point can be presented on List or a chart.
  • This is a statistical technique for trend analysis

Time Series Analysis?

  • Time Series analysis is a way to analyzed time data to get important statistics and other useful points of time series data.

Time Series Forecasting?

  • Time series forecasting is use of a time series model to predict future values based on past observed value in time series.

Applications

  • Stock Market Analysis
  • Economic Forecasting
  • Sales Forecasting
  • Financial Market Analysis
  • Inventory Management
  • Yield Projections
  • Military Planning
  • Marketing and Sales Forecasting

Lets explore some basic terms used in time series.

Time Series

  • Machine Learning dataset contain a collection of many attributes or observation.
  • Example
    • Attribute
    • Attribute
  • In time series dataset a time attribute is added. As Time play a very important role to predict value of future dates.Example
    • Time, Attribute
    • Time, Attribute

Time series components

Trend

It is a increase or decrease of behavior of data over a period of time. It can be linear or non-leaner. If there is upward or increase behavior called as Up-Trend, same for decrease know as Down-Trend. When there is no trend that is known as stationary trend or horizontal trend.Trend appear for some time and disappear.

  • Example :-A packers and movers open new office at newly constructed society. That will get good business over a period of time like 6 Month until all houses get filled. After that it will be no or very less business.

Seasonality

  • It is very much similar to trend but in repetitive in nature.
  • It is also increase and decrease in behavior data over a period of time.
  • Example : – sales increase each year on black Friday. Gifts purchases increase each year on Christmas and other festivals and it happen each year.

Noise/Irregularity

  • Variability in data that cannot be explained by model. It also called as residual.
  • It happen for a small amount of time and non-repeatable mode.
  • Example :- earthquake in a city can increase demand  of medicine and some other day to day living stuffs. But it will end once everything is fine. It happen once and not predicable.

CYCLIC

  • It is known or describe as a non-fixed pattern for more than a year amount of time.
  • It can happen any time like 3 years, 5 year and keep repeating and hard to predict.

When this not applicable 

  • When data is constant that time then this is not applicable.
  • When data is in form of a functions, where we can put function to get result.

 

I am going to cover in-depth concept and particle aspect in coming posts.

Keep reading….

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Gulab Chand Tejwani
 

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