Trend-The three sorts of trends are Uptrend, Low trend, and horizontal trend. Let me put an example of a trend, so there is a new township opened and someone started a hardware shop in there. Now what will happen, the people who are going to accommodate there will buy stuff from that shop and the sales of that hardware shop will go up and time series will show an uptrend. Once every house is settled will then there will be low sales, showing a downtrend. And once the trend graph will not go up and down but stay static will become a horizontal trend. The trend is something that happens for some time and then it disappears.
Seasonality- A repeating pattern is a fix time period, just like every year, the business of sweets rises up in the festival season. This repeating pattern doesn’t change but repeats the same business on a seasonal basis.
Irregularity- This component is best defined as the- let’s suppose if any calamity happens the sale of a particular medicine or ointment increase, which is erratic and once the people are healed then again the sales will be back on its pace. So Irregularity happens this way and affects the time analysis and a number of sales can’t be measured.
Cyclic-No fixes pattern, keep on repeating a very tough to predict and repeat up and down movement.
Data Analytics includes this major topic which is time analysis. It is beneficial in many ways and is one of foundation algorithm which is applied by all data scientists.
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