Why time series analysis is an important technique in statistics
Time series analysis enables researchers and data analysts to identify and understand the underlying causes of patterns or trends over time. Businesses, for instance, use data visualizations to pinpoint seasonal trends and examine why these trends occur. Using the robust analytics platforms and tools available on the market today, data visualizations can go beyond basic line graphs. When companies study data over consistent time intervals, they get useful insights that enable them to forecast the probability of events to happen in the future. Due to the ability of time series analysis to provide future forecasts, this technique is considered a part of predictive analysis. With time series, researchers can see potential changes in data, which makes it easier to understand data variables and make better future decisions. Modern technology enables businesses to gather huge amounts of data every day, making it easier than ever to collect sufficient data for in-depth analysis.
When to use time series analysis
Time series analysis is used to study stationary data, that is, events that fluctuate constantly over a given period or are influenced by time. It is one of the most commonly used statistical analysis in the retail, economics, and finance industries because sales and currency are always changing. The analysis of the stock market is a great example of the applications of time series analysis. Forecasting weather changes also involve analyzing time-series data, and this helps meteorologists make the right predictions from the day’s weather to future climate changes. Practical applications of time series analysis include analyzing weather data, measuring rainfall, reading temperature, monitoring heart rate, monitoring brain functions, analyzing stock prices and quarterly sales, trading stocks, and forecasting industry trends. Because time series analysis deals with many variations and categories of data, researchers may sometimes create complex models. But researchers cannot account for all the variables used in the model and they can’t use one model for every data sample. Complex models or those that try to perform too many functions at a time may lead to a lack of fit. Such models and those that are overfitting cannot differentiate between true relationships and random errors, which leads to skewed analyses and incorrect forecasts.
Types of time series analysis
Time series analysis incorporates a host of techniques that enable researchers to achieve different results. Some of these include:
Classification:Discovers and assigns various categories to the data being observed
Curve fitting:Enables researchers to plot their data on a curve to study how different variables are related to the data.
Descriptive analysis:Recognizes patterns in the time-series data such as cycles, trends, or seasonal variation.
Explanative analysis :Tries to understand the relationship between data as well as the cause and effect.
Exploratory analysis:Illustrates the main properties of time series data in a graphical format.
Forecasting:Predicts the future data based on previous patterns and trends.
Intervention analysis:Examines how a given event can alter the data being observed
Segmentation:Divides the data into sections to display the underlying characteristics of the initial information.
Classification of times series data
Time series data is information that has been collected over time, but many different types of data explain when and how the time when the information was collected was recorded. Based on this, time-series data can be categorized into two major classes:
Stock time series data:which involves measuring properties at a given point in time, like taking a stationary snapshot of the data as it was.
Flow time-series data:which involves measuring the activity of the properties over time.
Also, variations can occur occasionally through the collection or analysis of time series data. Some of these variations include: