Time Series Forecasting
What is the need of Time Series Analysis?
• Time series analysis is one of the algorithms of data science which is used for predicting the value.
• Unlike other algorithms, time series analysis deals with only single variable that has dependency on time.
• This method is extensively used in financial & business conjectures on the basis of historical data that has been collected over a certain period of time and is later compared with the latest trends.
An overview of Time Series Analysis
• It is a series of data which has indexed the points as per the time order. With the help of time series analysis a model is created that helps in predicting the future values which is based on the past values.
• It extracts the meaningful statistics and characteristic about the data and the purpose that time series analysis serves is to identify the trends, cycles and seasonal variances that assist in the forecasting of a future event.
• And in the presence of time component time series analysis is used.
When Time Series Analysis is not used?
Listed below are some of the scenarios in which time series analysis is not used at all.
• In the case of constant values time series analysis are never used. For example in the previous month a company sold around 30 cars and in this month as well the number of sold cars is same so time series analysis will not be used as the values are constant.
• Secondly, time series analysis will not be used during the time when values are represented using a function. For example you’ve used the function of ‘Sin X’ so there would not be any point of using time series analysis because you’ve already received the value but putting it in the function as well.
Components of Time Series Analysis
There are three components of time series analysis that are given below.
1. General Trend
2. Seasonal
3. Irregular Fluctuations
GENERAL TREND
General trend is the first component of time series analysis that tells about the values that whether they are increasing or decreasing.
SEASONAL
This is the second component of time series analysis where series are influenced as per the seasonal factor and this component is also termed as ‘Periodic Time Series’ as seasonal patterns are fixed.
IRREGULAR FLUCTUATIONS
Irregular fluctuation is the last component of time series that comes in the picture during the time of uncontrolled situations that occurs when the time value changes.
So this was all about time series analysis that has a major role to play in the field of data science. If you are willing to learn about time series analysis and looking for any IT institute that provides the best training of it then SkyWebcom is one such IT institute. SkyWebcom is the most reputed IT institute of training that offers the best Data Science Training in Noida. Training is offered by data science professionals who has 32 years of experience. At SkyWebcom data science course is very extensive that ranges from basic to advance level. SkyWebcom provides Online Data Science Training as well for those who are unable to attend the live sessions. Apart from comprehensive training SkyWebcom gives 100% placemnet assistance in top companies as well. So, make your career paths strong by joining SkyWebcom.