# The Business Applications of Time Series | Learn Data Science

While the world is trying to force deep learning onto every problem imaginable, big or small, there’s a humbler yet effective technique that’s been quietly chipping away in the background building up almost quintillion business applications over time. Time Series Forecasting is not just your typical machine learning solution that learns from past data. In addition to this, it factors in time to learn that depending on the time of day/year/month/time period, certain events are likely to either happen again or move in a certain direction.

Let’s pause for a moment and think about all the events in our lives that are heavily dependent on time or periods over a lifetime. From growing to ageing, graduating to retiring, one place to the next – we all have our own individual sequence of events that took place over time in our lives. Now think about all the events we could collect data on that are also heavily dependent on time. From traveling to and from work, to ordering food, to purchasing an item, to reading your news feeds, to the Earth orbiting around the Sun. There’s a time associated with almost anything. So we can collect data on almost anything at any time interval. (I say ‘almost’ because unless you want to figure out a way to collect data inside a black hole, for example, almost everything can have a regular timestamp.)

## What is Time Series Forecasting?

Time Series data are observations on something recorded at regular time intervals (univariate/single variable series). Time Series Forecasting looks at data over time to predict what will happen in the next time period, based on patterns or re-occurring trends of previous time periods. These patterns could be seasonal, where there’s a periodic trend or re-occurring event, or it could be a consistent upward/downward trend over time, or it could be no recognizable pattern or trend over time.

Understand Time Series in minutes

One of the most common techniques for Time Series Forecasting is Autoregressive Integrated Moving Average (ARIMA). This technique predicts the next timestamp ahead by both regressing and averaging over the previous data values.

## Key Business Applications Outside Finance

Besides your typical financial modeling, there’s so much more Time Series Forecasting can apply to, especially when predicting demand. It would be a disservice to only limit Time Series Forecasting to the world of finance. Here are 5 distinctly different business applications of Time Series Forecasting to give you a good sample, albeit a very small one that doesn’t even make up a fraction of the tip of the iceberg.

### Scenario 1: Forecasting Online Users

A Time Series model predicts ~600,000 people to login online in the next few hours. The online sports streaming platform already knows there would be a lot of people online due to a big event happening then. But now it can better plan for how many additional servers and infrastructure needed for the online platform, based on how many online users are predicted. Also, those servers are only used for that particular time of day, switching them off for the rest of the day to save money. Another Time Series model predicts a significant increase in online users from last year and even more so the year before then. The company decides it has reached a point of continuing significant growth, and now is the right time to invest in better infrastructure for the year ahead and coming years ahead.

### Scenario 2: Forecasting Traffic

A sensor device records the number of vehicles that cross an intersection every 20 minutes. Using these counts of vehicles taken every 20 minutes, a Time Series model predicts that in the next 20 minutes traffic at the intersection is likely to spike to a hugeamount. Now your trip planning app decides to re-route you to avoid this congested, problematic intersection, distributing the traffic load more evenly across roads.

### Scenario 3: Forecasting Customer Satisfaction

Customer reviews are web scraped and analyzed every day, with an overall score on their sentiment that shows whether they are happy with the company or upset. A number anywhere from -1 (most upset) to +1 (most happy) is recorded each day. The company has seen a turn of events where the score has started to drop gradually over time. The company is thinking about whether it should hold off on acting on this right away and save spending time and resources, as it might turn around to be positive again. A Time Series Forecast says it is not likely to get any better, and in the next few days it will continue to drop to a score that is unacceptable for the company. Before potentially reaching that point, the company has decided based on this model to pull in extra resources to assist the customer service team so that they can pay extra special attention to customers and hopefully turn that trend in the right direction.

### Scenario 4: Forecasting User Spending Habits

An online retail site usually sees periods of peaks and falls in its sales. However, ordering stock during peak times is far more expensive than during off peak times. Last year during peak season the retailer had to order extra stock at a premium price because they underestimated purchasing demand for their product. This year the retailer is wiser in that it is using forecasting models to predict or get a closer estimation for how much stock is needed for the next peak season ahead, instead of using their best guest. The model says their best guess for this year is still underestimating how many customer purchases are to come during peak season. The retailer decides to order extra stock than expected during off season before it become exuberantly expense to order extra stock during on season.

### Scenario 5: Forecasting Staff Turnover

Many factors can lead to staff turnover, but have you ever noticed that there might be certain months in the year where turnover is a bit higher than others? Perhaps recruitment agencies use the New Year to target New Year career goals and pay to people, or leaving a company during a less busy time period allows an employee to change jobs smoothly. Sourcing new hires and onboarding can be time consuming and expensive. A company would like to see what a Time Series model predicts in terms of which months they could expect a higher turnover, so that they can implement employee retention plans before then.

What are some business applications that you can think of where Time Series Forecasting would be useful?

To learn how to build a Time Series model and forecast, watch this video series.

This is a companion discussion topic for the original entry at https://blog.datasciencedojo.com/time-series-business-applications/

Great read! As a former Bootcamp student; I value all the resources that the DSDojo has available. What is your experience/opinion on the HTS R package for grouped time series? I believe I have a dataset that would be a good fit for it. Having a hard time structuring the raw data into the format required. Anyone know of same sample data/exercises using HTS?