Everything You Need to Know About Time Series Analysis
This article explains what time series analysis is and also the importance of using it in certain situations to provide valuable data insights.
Join the DZone community and get the full member experience.Join For Free
In business, you need to know how your business is doing day-to-day. In order to understand the health of your company, many turns to data analytics. Time series analysis can give valuable insight into what has happened over the course of days, weeks, months, or even years. With this form of analytics, you can learn if your company is making more money during the summer months or at the beginning of the fiscal year. You can also analyze weather data to see if winter storms caused any loss in revenue.
Some questions that time series analysis can answer are: How many customers come into the store every day? How much revenue has the business generated over the last few weeks? What is the probability of rain tomorrow? It could be helpful in planning future actions, saving money, and ensuring quality.
What Is Time Series?
The sequence of data points indexed or are based on time and are recorded in successive order are called time-series data points.
What Is Time Series Analysis, and What Is Its Importance?
Time-series data analysis is the way to predict time series based on past behavior. Prediction is made by analyzing underlying patterns in the time-series data.
E.g., Predicting the future sales of a company by analyzing its past performance.
Predicting the state of the economy of a country by analyzing various factors affecting it. These series are generally time-series, and they contribute to the economy.
The importance of time series analysis for science, industry, and commerce, is as follows:
- The study of past history is necessary for forecasting future events.
- Time series analysis shows why trends exist in past data and how they may be explained by underlying patterns or processes.
- Time series analysis is a basic tool for the analysis of natural systems, which cannot be understood without it. For example, climate cycles and fluctuations in the economy, as well as volcanic eruptions and earthquakes, are examples of natural systems, whose behavior can best be studied using time series analysis.
Time series analysis gives a way to predict the future. It is essential in engineering, finance, business, and the economy to make it easy for investors, customers, or engineers to make the proper decisions.
E.g., If we know how the weather will be tomorrow, we will be ready for it before we go outside. Or, If we know the future value of stocks, we can decide whether to invest it in or not.
Major Components of Time Series Analysis
Given the discrete nature of time series, the data is often taken from discrete time periods. Here are the 4 major components:
- Trend component
- Seasonal component
- Cyclical component
- Irregular component
1. Trend component: This is useful in predicting future movements. Over a long period of time, the trend shows whether the data tends to increase or decrease. The term “trend” refers to an average, long-term, smooth tendency. Not all increases or decreases have to occur simultaneously. Different sections of time show varying tendencies in terms of trends that are increasing, decreasing, or stable. There must, however, be an overall upward, downward, or stable trend.
2. Seasonal component: The seasonal component of a time series is the variation in some variable due to some predetermined patterns in its behavior. This definition can be used for any type of time series including individual commodity price quotes, interest rates, exchange rates, stock prices, and so on.
In many applications, seasonal components can be represented by simple regression equations. This approach is sometimes referred to as a “seasonalized regression” or a “bimodal regression.”
3. Cyclical component: The cyclical component in a time series is the part of the movement in the variable which can be explained by other cyclical movements in the economy.
In other words, this term gives information about seasonal patterns. It is also called the long-period (LP) effect or boom-bust process. For example, during recessions, business cycles are usually characterized by slower growth rates than before the recession started.
4. Definition of irregular component: The irregular component is the part of the movement in the variable which cannot be explained by cyclical movements in the economy.
In other words, this term gives information about non-seasonal patterns.
This term refers to changes that are not cyclical. These include boom-bust processes, permanent changes in the long-term trend of a variable, or “not seasonally adjusted” information which is not normally found in national income and product accounts (such as depreciation, research and development expenditures, and agricultural subsidies).
What Are Stationary and Non-stationary Time Series?
The time series which has constant mean and variance is called stationary time-series. It is recommended to have the stationary time-series for better analysis. The predictions on non-stationary series may give wrong values.
To check whether a series is stationary or not, there are several tests in the literature. One of them is the Augmented Dickey-Fuller (ADF) test which is a unit root test. Its null hypothesis is that the series is non-stationary. If the p-value is less than 0.05, the null hypothesis can be rejected, and the series can be considered stationary.
How Do You Make A Series Stationary?
A series can be made stationary by various methods like:
- Difference Transform: Subtracting previous value with current value is called differencing. It is done to remove the dependency of values on time. One can check the differenced series with the ADF test for stationary.
- Second differencing: If the result of the ADF test on the differenced series shows that the series is still non-stationary, then one can subtract the differenced series again.
- Removing trend and seasonality by using HP-filter or band-pass filters and X12 ARIMA analysis.
Is It Necessary To Remove Trend And Seasonality?
No. There are some models like Prophet, SARIMAX, etc., which take care of seasonality while modeling. The basic ARIMA model needs the de-seasonal data.
What Are the Objectives of Time Series Analysis?
- To study the trend of the series
- To compute the time-series data
- To create a new data set from the existing one
- To analyze and compare the old and new data sets
- To detect the causality among the variables of the data set
- To study cross-sectional relationships between different types of variables
- To interpret the economic significance of the series data and their relationship with other factors in the economy
Published at DZone with permission of Prasanna C. See the original article here.
Opinions expressed by DZone contributors are their own.