As illustrated across our website, time series are everywhere. Each industry vertical, each domain, each company comes into contact with time series in one way or another. In the use case library below, you can explore the extensive applications of time series, or look for applications in your specific field of interest.
Anyone who has ever sat in an S&OP meeting or in budget review will have heard this before: “We would have hit our target if it wasn’t for the (rain, wind, snow, sun, cold, hot…).” Very rarely are these comments backed up with real facts – but you will get the occasional nod around the room. Ah yes… I do recall it rained on the second Saturday in July – that must be why we missed our target by 12%. Or the line in a seasonal review meeting that says, “Our winter boot sales are up this year over last year because of the early winter storm this year and the late Indian summer in 2017, remember???”
The problem with these statements is that though they may be true, mostly they are easy excuses that cover true problems within the business. Maybe there are supply issues, or merchandising issues or a general decline or growth of our brand. Instead of comparing actuals to budget, what if a third line could be added called “Event adjusted budget”. This way, managers, planners and executives can understand the impact that events, such as weather, may have played on our performance, but most importantly, this will help us to understand and think about what else might be happening in our business. Sounds interesting?
The good news is that with advances in Machine Learning and tools like the TIM engine, this is entirely possible to do. By comparing actual sales history at different levels of your organization and bringing in weather conditions on the day as features, a company can start to understand the importance of these features on driving actual sales. Using this data, we can start to see what categories are heavily impacted (ice cream, umbrellas, or the heating bill) and what locations get impacted (outdoor shopping malls, online sales, restaurant delivery). As the TIM engine begins to understand what event variables are driving volatility, we can create historical forecasts based on simulated conditions. (This is what we would have predicted this year based on the same conditions as last year as an example.) This means that the next time your sales rep argues they would have made their target if it wasn’t for the cold front, your business can support this with real evidence. Not only that, but you can start to use live weather forecasts or long-term weather trends to drive predictive interventions – like adding an extra employee at your beach café this weekend.
With cloud storage being so cheap, detailed datasets containing years of history for hundreds of thousands of weather data points are readily available. Not to mention other event data like football games, traffic patterns, holidays and much more. This data is relatively easy to find and there are many services that make this data available historically and as forward-looking projections. With this data and your historical sales and historical budgets, the TIM engine can train models to produce forecasts based on simulated conditions (or after removing them entirely).
Watch a video taking you through this use case below:
Looking for a more in-depth view into this use case? Check out this video:
Many cities around the world have adopted a bicycle sharing system. These systems allow people to cycle around the city, picking up a bicycle at one docking station and dropping it off at another. Not all docking stations experience the same use, however. Some stations might be popular starting places, leaving them empty at the end of the day, whereas others see many people arrive throughout the day and end up completely occupied. Understanding and predicting these patterns goes a long way in the planning of the redistribution of bicycles accross docking stations that often happens at night. Yet the situation is even more complex, since people’s behaviour is influenced by many other factors, such as the weather, time of day and calendar. Forecasting the usage of bicycles also gives insight into the demand, allowing cities to anticipate higher demand by expanding the bicycle network, for example.
TIM can help in these scenarios, by taking into account many different variables, transforming and weighing them in order to produce accurate and understandable forecasts. Moreover, TIM allows for frequent model rebuilding, quickly adapting to meet challenges posed by sudden and unexpected changes in peoples behaviour. In case of any issues with data collection, TIM is also able to account for changes in data availability. Thanks to the explainability of the models, it is possible to develop a sense of understanding at what constitutes an accurate forecast, and what influence changes in predictors may have. This can serve as a start for answering questions like “How would the necessary redistribution change if it was colder than expected, tomorrow?”
Apart from historical values of the amount of bicycles in use, this use case takes calendar data, weather data (temperature, windspeed, wind feel, humidity…) and the time of day as input. The generated models then produce forecasted values of the amount of bicycles in use, in the same granularity as the target input data.
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