Realtimecampaign.com Shares a Beginner’s Guide to Time Series Analysis

December 14 21:45 2020
Realtimecampaign.com Shares a Beginner’s Guide to Time Series Analysis

Time is a crucial factor in recording and organizing data. Anyone who has stayed up-to-date on the latest Covid-19 pandemic trends is more familiar with time-series data than they might realize. Those interested in learning more about time series data can take a look at this site.

Raw time series data needs to be broken down and organized so it paints a picture of what the data set represents. The process of collecting these data points over a period of time is called time series analysis. It is an essential practice in several modern applications, from following global pandemic trends to self-driving cars.

Time Series Analysis Explained

According to realtimecampaign.com, the process of time series analysis is used to identify three aspects of a set of time-series data points, which are autocorrelation, seasonality, and stationarity. Autocorrelation refers to data patterns that repeat but do not do so at regular intervals.

Seasonality refers to data patterns and observations that repeat themselves at regular intervals. For example, a graph showing the temperature fluctuations in a region over several years allows a person to see specific trends during summer, fall, winter, and spring. 

Stationarity refers to how infrequently data observations and patterns change over time. Using a similar example to seasonality, temperature information recorded in an area over ten years would have little variance overall. 

Benefits of Times Series Analysis

Data analysts are charged with the task of translating data set points into more digestible information. Modern technology allows data analysts to gather loads of useful data at lightning-fast speeds, as seen in the article Timescale Launches Industry’s First Multi-Node Relational Database for Time-Series Data, Giving Organizations Unprecedented Scale Time series analysis allows these individuals to clean, understand, and forecast data for practical use. 

  • Cleaning Data. Cleaning data involves eliminating data point outliers or anomalies and applying various averages to a data set. This process makes it easier to identify the true data set. After cleaning the data, analysts can get a clearer perspective on the information the data provides overall.

  • Understand the Data. Time series analysis services, such as those offered by companies like Timescale, help business analysts understand the data set. Visual models help pull data together to form a complete picture of past data points. This leads to the next benefit, which is forecasting data.

  • Forecast the Data. One of the primary reasons for time series analysis is to predict what will happen in the future. By identifying patterns and creating a visual understanding of the past, analysts can use forecast models to adjust future data set points’ expectations. 


Time Series Analysis Applications

Time series analysis is an invaluable resource in business development across a wide range of industries. Places, where it is easy to see the benefit of time series analysis, include weather data, heart rate monitoring, stock prices, industry forecasts, automated stock trading, and interest rates.

The importance of time series analysis cannot be overstated. It is not a new development, but modern software has taken it to the next level. It is an advanced subset of data analysis with the potential of skyrocketing a business’s success.

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