What is Time Series Forecasting: Applications and Examples

 Having artificial intelligence and machine learning as part of services– distinguishes the business from others in the race. Technology has changed everything from healthcare to fintech to ease the routine of everyone.

In the meantime, future prediction through data analytics and time series forecasting has changed the way businesses operate and make decisions.

The time series forecasting gives an overview of how business is doing in a day-to-day routine. It provides insights into what the company did in days, weeks, and months.

What is Time Series Forecasting?

The time series prediction is a process where real-time predictions are made based on historical time-stamped data. These predictions help businesses to know about the current worth, asset values, and future decisions.

For example, some questions that time series analysis can answer: How many liters of crude oil were extracted from wells in the last 7 days and how can it be enhanced? These types of questions can be answered by bringing out time series forecasting.

Now, let’s discuss some real-world applications of time series prediction on time-stamped historical data.

Applications of Time series Forecasting

In machine learning, time series forecasting has a wide range of real-world applications which eased businesses as well as human life. These applications are solving critical problems by suggesting time series analytics. These applications are:

• Weather Forecasting

• Production Forecasting

• Climate Prediction

• Economic Forecasting

• Healthcare Forecasting

• Finance Forecasting

• Retail Prediction

• Environmental Studies Forecasting

• Social Studies Forecasting 

• Time Series Prediction in Business Domains

• System Maintenance Forecasting

• Business Development Prediction

• Supply & Demand Forecasting

Examples of Time Series Forecasting

These are limitless realistic examples where time series forecasting using the machine & deep learning helps businesses grow. For instance, if we talk about the energy and commodity sector, time series forecasting allows the oil and gas system to predict supply and demand according to different regions, timestamps, and market volatility.

1. Weather forecasting uses instantaneous data labeled with time stamps to predict the next day, week, and month climate conditions.

2. Trade market pricing prediction on historical data.

3. System maintenance via implementation of expert systems to keep everything in check through time series forecasting.

4. Retail forecasting in departmental stores and multinational confectionery items producers.

5. Sales and production prediction based on historical data and analyzing newly developed product demands.

How Time Series Prediction Helps Businesses

The addition of artificial intelligence and machine learning has changed everything by implementing different computer science algorithms and techniques. Among other methods, time series forecasting on historical data services companies to grow exponentially via process automation and business growth. The following are some real-world use cases of how time series forecasting is making a difference in businesses.

• Assist in trend estimation in collected data points.

• Predict the business growth and streamline the decision-making process in regular routines.

• Seasonal patterns or trends prediction for quarterly distributed data.

• Time series forecasting or prediction helps businesses in automation.