Trends are main characteristics of everything that changes with time. It is applied to many fields such as social, political and economic areas. Trend analysis is a vast field that studies the common behavior in different entities, objects or subjects.
It is a valuable tool in data science and machine learning. There are many types of trend analysis that can be used to predict trends in various domains like government, retail, finance, health care and energy sectors etc. Data mining and visualization techniques are used to detect trends used by different companies. This article will explain how trend analysis works and list some examples of where it is applied.
Trend analysis is the process of identifying and analyzing emerging or existing trends in the data. In this post, we will discuss different types of trend analysis that are used to identify patterns in historical data. We will also use python to analyze different types of data.
It can help you identify emerging markets, pick up early indicators for sales and profits, predict customer behavior, and find more about your customers by looking at their past behavior.
Most organizations use trend analysis as a part of their ongoing business strategy to improve their performance and make better decisions.
There are many types of trend analysis, but they all have one thing in common: they all involve the use of data science to interpret and predict current and future trends in a given industry or market. Here are some examples of trend analysis :
- Pattern recognition
- Scenario planning
- Technological forecasting
- Consumer forecasting
- Budgeting
Here, I will be talking about a few methods or techniques which we can use to identify trends while working on analytics or data science projects. Let’s start with the elements that we will need for this :
- Problem statement/discovery topic
- Data set about point 1
- Clarity on what is that you are trying to understand (End objective)
- Checking data relevancy
- Framework to deliver the message
Once we have all these ready then let us see some of the methods
- Timeseries Trend: This is analyzing data over some time to identify if it is the direction of movement. It can Upward Trend, which means something is increasing in volume or price over some time. Downward Trend, as you must have guessed, is opposite to Upward Trend like something is decreasing over a while and lastly, Horizontal trend means it is neither moving up nor down they are ideally moving sideways.
- Geographical Trend: It is often analyzing trends with a set of people or groups either by location or region
- Temporal Trend: Trends are sought about a baseline, either within a single time series or throughout time.
- Intuitive Trend: Examining trends based on demographic and behavioral tendencies, as well as other characteristics deemed important.
In Data Science we can use various formulas like Linear, Quadratic, Exponential Growth, S-curve, Forecasts, MAPE, MAD, MSD to achieve some aspect of trend analysis
Marketing executives create trending campaigns and monitor the effectiveness of existing campaigns to ensure a profit for their company. To do this, they need to be able to detect trends in different sections of the campaign and take steps to correct them in order to achieve their marketing objectives.
Data science methods are used to find answers for such strategy questions as how well a trending campaign is performing or how many customers will be attracted if a new product is introduced into the market or offered at a lower price.
To get an idea of the strength of a trend, data scientists would also like to know how long it is likely to continue. With a strong trend these days, there is enough evidence to assume that the trend will continue for some time, but to give a more accurate guess as to when the trend might end, we need stronger signals of a reversal
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