# 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.