What Is Time Series Forecasting?

Businesses use data to help make decisions, and one key way to use that data is to study patterns that emerge over time. Data collected over a long period helps highlight important patterns and allows business leadership to make informed strategies that bring them closer to stated goals. The more data available from the past, the easier it is for a business to anticipate the future. Time series forecasting is one such method for gathering and analysing long-term data. Read on to learn more about time series forecasting, how it differs from time series analysis, what benefits it offers to data science, and how to pursue a career in this exciting field.

What is time series forecasting?

Time series forecasting is a scientific method in which predictions are made based on data collected over time. Data scientists record time series data at set intervals, whether daily, weekly, or yearly. These intervals have no set minimum or maximum time frame, but typically, more data means more accurate forecasting. Once data professionals have collected enough time series data, they can use it to predict future actions or habits and recommend action plans accordingly.

What are the components of time series forecasting?

The components of time series forecasting include the trend, the cyclical variation, the irregular variation, and the seasonal variation.

• The trend is a single, long-running pattern that emerges from the data, such as the general increase in temperature as summer approaches.

• The cyclical variation is fluctuations in data that occur over one year.

• The irregular variation involves inevitable and uncontrollable fluctuations in data, such as natural disasters.

• The seasonal variation is quarterly or monthly data and reflects rhythmic patterns during the year.

Time series forecasting vs. time series analysis

Time series analysis is a component of time series forecasting. Data professionals use data analysis to forecast or predict future behaviour, patterns, and business decisions. Once data professionals collect enough data, they analyse it for insights catering to their business’s goals. Then, that analysis helps to inform future strategies and decisions by removing the guesswork of what may or may not occur based on the data.

Applications of time series forecasting

Data professionals use time series forecasting in a wide range of industries. Some examples of these industries include:

Stock market forecasting

Stock market professionals use forecasting to help predict patterns in stock market prices. Because of the stock market’s history, professionals can look at data from huge time ranges, such as decades, to identify a particular company’s buying and selling habits. These past data ranges inform their recommendations for purchasing or letting go of different stocks.

Analysing seasonal industry trends

Seasonal industry trends help businesses identify patterns based on the seasons, such as the demand for certain products during certain times of the year. These trends also help companies stock items correctly or prepare for weather-based issues.

Analysing and predicting climate patterns

Data professionals use time series forecasting to analyse and predict climate patterns that positively and negatively affect industries such as agriculture. For example, time series forecasting helps predict rainfall patterns that influence the production and prices of India’s agricultural commodities.

Predicting health care needs 

Time series forecasting can also provide essential information for predicting infection and disease patterns that influence how hospitals and other health care settings prepare for large-scale issues. One prominent example includes how professionals used time series forecasting for the COVID-19 pandemic to help predict future positive cases. Having this type of estimate can assist governments when allocating resources and keeping cases under control.

Who uses time series forecasting?

Many data professionals use time series forecasting and data sets to improve future decisions and strategies. Some examples of these professions include the following:

Data scientists

Data scientists collect, analyse, and interpret data. Then, they use those insights to make recommendations for action or strategies. Data scientists often have strong backgrounds in statistics, databases, coding, and machine learning. To step into this role, you’ll typically need a bachelor’s degree in math, economics, or computer science. The average annual salary for a data scientist is ₹14,50,000 [1].

Data analysts

Data analysts use data to make more informed decisions that help a business reach its stated metrics or goals. They use market research and consumer behaviour to create data-driven plans and strategies. Data analysts typically need a bachelor’s degree in mathematics, computer science, or finance. The average annual salary for a data analyst is ₹7,70,000 [2].

Machine learning engineers

Machine learning engineers create autonomously running models and systems that interpret data collected or fed to them. They use theoretical modelling to create tailored models and process real-time data. You’ll typically need a bachelor’s degree in computer science or something similar for this role. The average annual salary for a machine learning engineer is ₹11,46,000 [3].

Pros and cons of time series forecasting

Time series forecasting has various pros and cons. Some benefits include the following:

• Data professionals can predict future patterns accurately and efficiently.

• With more data points that professionals collect over time, professionals can draw more insights from an analysis.

• Professionals can learn why past patterns occurred and how to better prepare for the future.

Some cons include the following:

• The process requires an intensive, long-ranging amount of data to be useful.

• Small time intervals provide less accurate information.

How to get started with time series forecasting

To start a career or learning path in time series forecasting, you’ll want to develop a background in computer languages, statistics, and data modelling. A bachelor’s degree can provide a good start, especially if you pursue this subject as a career. 

Then, you’ll want to decide what forecasting model will best address the questions you want to explore. You can use open-source software like Python to build your desired model. Once you’ve collected the necessary data, you can use data visualisation tools to present the information in a useful way.

Learn more with Coursera.

Time series forecasting is useful for creating data-driven decisions that improve outcomes for businesses and organisations across various industries. You can also explore courses and certificates to learn more about time series forecasting. With options such as Institute for the Future’s Forecasting Skills: See the Future Before it Happens on Coursera, you’ll learn about the foundational aspects of time series forecasting and how it may benefit your future career.