What is time series forecasting?

Time series forecasting is a type of predictive analytics focusing on some aspect of time, whether overall trends or cyclical patterns. Many statistical models attempt only to correlate one or more variables with the likelihood of a given outcome. They assume that under the same conditions, the same outcome should always be equally likely to occur. However, sometimes those probabilities change over time, leading to reduced accuracy at best and in many cases entirely ineffective predictions. Time series data forecasting considers not only the particular conditions but also the time that events occur in order to better understand and predict outcomes.

Analyzing vs. forecasting

Time series forecasting is closely related to time series analysis, but there is one key difference: Time series analysis focuses on understanding data from the past, whereas time series forecasting is interested in predicting how events will turn out in the future. Time series analysis, however, can still play a role in developing models that are used in time series forecasting, so its importance goes far beyond simply understanding historical patterns.

Why is time series forecasting important for your business?

The likelihood of most events is influenced by time. Whether it is rising or falling cost or consumer interest, the time of day that people tend to sleep, or the season that someone is most likely to travel, models of these patterns that fail to account for time are vastly less effective at creating accurate predictions than those utilizing time series forecasting.

With time series forecasting, a shipping company can easily study which days of the year and which seasons have higher or lower than average shipping utilization. With this data analysis, companies know when to add seasonal hires or limit the number of employees on vacation. Predicting weekly patterns of demand allows the company to account for fluctuations, even simply during the time it takes a package to move from source to destination.

What are the components of time series forecasting?

Base Level
The base level is how given conditions correspond to the outcome without considering time. This is the limit of what other techniques can model, but it’s only part of what time series forecasting considers for its predictions.

Global Trend
This is the simplest time-related component of forecasting. This describes the overall trends in the data, such as whether the outcome is becoming more or less likely over time. For example, the famous “Moore’s Law” essentially states that the complexity of microchips tends to double every 18 months; so time series forecasting of microchip manufacturing would include a global trend of doubling performance every year and a half.

Cyclicality
Some trends follow a repeating pattern at regular intervals, and this is described by the cyclicality part of time-series forecasting.

Telecoms need to plan their infrastructure around levels of consumption that vary over the course of the day or the week to effectively handle the increase in demand from customers outside of traditional work hours. 

For retailers, the cyclicality of holiday spending is so important they build their entire financial year around understanding the spike in sales–most famously, Black Friday. 

What should I look for in time series forecasting tools?

Quality time series forecasting tools are flexible, and can not only process existing data but also update as new data arrives. Tools should be able to understand both general trends and cyclical patterns as well as leverage all your existing analytical infrastructure.

Domo’s time series forecasting tools are the perfect extension of an already world-class data package. Create complex forecasting charts over any data set within just a few clicks, leveraging the benefits of time series forecasting across your entire organization. With Domo’s advanced data governance and rules-based access, every person in the business is able to access the data they need to make data-driven forecasting decisions.  

How do different industries use time series forecasting?

Almost any industry can benefit from the insights of time series forecasting. Here are a few examples:

• Predictions: Anticipating significant future events can be anywhere from useful to paramount for a range of professions. A key protection from flu each year, vaccination depends on anticipating which strains are going to be most common up to a year in advance. Forecasting the location and time of landfall for hurricanes is a task so important that lives depend on accurate reports on approaching storms from Meteorologists. 
• Planning: Running a business requires significant planning to be successful. Without time series forecasting, businesses like distilleries would have almost insurmountable difficulties with managing the resources needed to produce their product in appropriate quantities for consumers in five, ten, even twenty-five years when their product is fully aged and ready for sale.
• Patterns: Many business resources are utilized in ways that vary regularly, and understanding these patterns can be critical to maintaining the right amount of resources to meet demand without wasting them on times they are not needed. Without understanding patterns in network utilization, tech firms would need to maintain maximum capacity at all times to just meet peak demand, but this is enormously wasteful during all other times and could easily cost enough to bankrupt those who ignore these patterns. 

How will time series forecasting affect the future?

Time series forecasting can help anticipate future trends and identify risks and opportunities in time to plan accordingly. Using data-driven decision making leads to greater transparency and accountability in every organization.

With insight into the patterns, costs can be lowered over time, prices optimized, a demand met, risks planned for, pitfalls avoided, and efficiency throughout every aspect of business improved. It can even lead to higher satisfaction of employees and improve their daily productivity.