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Time Series Data: Definition, Types, and Analysis Methods

Last updated: Jun 21, 2024

What Is Time Series Data?

Time series data is a set of data that is accumulated and recorded at regular intervals. It contains information collected over time and is common in fields such as finance, signal processing, and meteorology.

You may encounter the term time series data regression analysis, or an analysis performed when response variables are autocorrelated (significant), creating a functional relationship.

Typically, functional relationships take the form of linear regression. However, the calculation of parameter estimator values in linear regression analysis cannot always be used as a benchmark.

 

Why Is Time Series Data Analysis Important?

As explained earlier, this data is used in various fields. You may need time series analysis to find out information from time interval data, such as seasonal patterns, variability, and seasonal trends.

Then, time series data analysis describes the sequence of data points collected in a certain time interval to help project price changes, value changes, stock price predictions, and so on.

 

When Do You Need This Analysis?

There are various purposes for using time series analysis. Typically, the purpose of the analysis is to identify cyclic or seasonal data patterns and predict future values based on the pattern of current and past values.

For example, businesses in the financial sector, which are often affected by seasonal trends, may find this analysis useful. The other objectives of time series analysis are as follows.

 

1.  Mitigating Risks

Analysis of time series data is used to understand historical patterns to determine potential present and future risks. This helps you to take the right precautions and make the right decisions.

 

2.  Recognizing Data Patterns

The next goal is to identify data patterns. This analysis helps you observe historical data so that you can identify recurring patterns over time.

 

3.  Making Decisions

Time series analysis is a good way to make decisions based on data patterns over some time. By understanding seasonal patterns, you can strategize accordingly.

 

4.  Predicting Value

Value prediction allows you to estimate future data patterns based on past data. This data will help you manage resource allocation and sales in the business.

 

Types of Time Series Analysis

Because time series analysis uses different categories of data, an analyst must match the data to the type of analysis. The types of time series data analysis are as follows:

 

1.  Curve Fitting

The first type is curve fitting, which uses mathematical functions on time series data to model and examine the relationships between variables in the data.

 

2.  Classification

Classification is another type of time series analysis that categorizes data based on certain criteria so that data patterns can be properly analyzed.

 

3.  Segmentation

Segmentation is a type of analysis that divides time series data into subcategories by criteria. This type of analysis can reveal characteristics of information that are not directly visible.

 

4.  Descriptive Analysis

Another time series data analysis is descriptive analysis, or analysis aimed at describing data patterns. This type is suitable for analyzing trends or seasonal cycles.

 

5.  Forecasting

Time series forecasting helps you calculate future data patterns based on historical data cycles. You can perform time series forecasting using extrapolation, machine learning algorithms, or Autoregressive Integrated Moving Average (ARIMA).

 

6.  Explanative Analysis

Explanative analysis goes deeper than descriptive analysis because it explains why a certain pattern of data can occur in a certain interval or time. This analysis requires variables outside the data that can affect the time series data. 

 

7.  Intervention Analysis

Intervention analysis examines the effect of interventions, such as policy changes, external factors, or other events that occur in the time series under study, on time series data.

 

8.  Exploratory Analysis

Exploratory analysis examines the basic characteristics of time series data to understand the data, identify patterns in the data, and find anomalies in the data.

 

Time Series Data Analysis Method

Here are several methods that can be used to analyze time series. 

 

1.  Holt-Winters Method

The time series analysis method is the Holt-Winters or exponential smoothing technique. It can estimate data that have a seasonal trend, which makes it suitable for analyzing short-term seasonal patterns.

 

2.  Box-Jenkins ARIMA Model Method

ARIMA or Autoregressive Integrated Moving Average is a model that can analyze and predict the data of time series with one variable. The ARIMA model is suitable for the analysis of stationary data where the covariance and variance are consistent. 

 

3.  Box-Jenkins Multivariate Model Method

Multivariate methods can analyze time series that have more than one variable, allowing you to find out the dynamics of interactions and relationships between variables over some time.

An example of using this model is to analyze the relationship between shoe sales and the weather over time.

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