Are you harnessing the power of your time series data?

Akash Srivastav, Principal Product Manager, Oracle

Every business tracks key metrics—sales, revenue, inventory, demand—to understand trends over time and plan accordingly. This is called simple time-series analysis.

We also use traditional statistical methods and spreadsheets to predict the future values of these business metrics, but often live with the risk of uncertainties due to unaccounted external factors, inefficient techniques, or missing data science expertise.

In both scenarios, whether using simple time series analysis or traditional techniques, we often fail to identify the hidden patterns or complex relationships among impacting variables. The result? Lost opportunities—and money. For example, in 2001, Nike used demand-planning software that underestimated the popularity of Air Jordan shoes. This ended up costing Nike $100 million in sales and share prices dropped by 20%, according to a press release by the company.

Machine learning has changed the game, however. Enter the new, fully managed OCI Forecasting service, which delivers time-series forecasts through advance machine learning and statistical algorithms without the need for data science expertise. Now, predictions are plug-and-play for time series data in any domain.

OCI Forecasting reveals hidden relationships

OCI Forecasting is a generic, plug-and-play AI service that handles single time-dependent variable series (univariate) or multiple time-dependent variables series (multivariate) problems for any business forecasting. What’s more, you don’t need any data science or machine learning expertise to access this public cloud offering via REST API, Oracle Cloud Infrastructure console, the command line interface (CLI), or software development kit (SDK).

OCI Forecasting quickly detects complex relationship patterns, trends, seasonality, errors, and exogenous factors in your data. It then provides accurate predictions, confidence intervals. Further, a competitive differentiator is the explainability of local and global influencing factors (such as promotions, prices, weather conditions) that OCI Forecasting provides.

There are three key features to know about OCI Forecasting—and they are all available in just one API call:

1. Automatic data preprocessing.Often a cumbersome prerequisite before dealing with algorithms, OCI Forecasting includes all the required preprocessing and does the job automatically. Several filling methods handle missing values, while others treat outliers, transform data, and aggregate temporal data (converting days to weeks or months).
2. Developer-focused AI.OCI Forecasting boasts a wide range of training algorithms, from commonly used statistical methods to complex machine learning and deep learning algorithms, and picks the best one based on the least ROCV (rolling over cross validation) errors. It comes with scheduling capability to include all the latest data points before forecast every time to capture even a small change in business trends.
3. Explainability and confidence intervals.Unlike its competitors, OCI Forecasting provides explainability as an output, which describes influential features at global and local levels in your data and brings transparency in forecasted results. It also provides confidence intervals, which are a range of variations in predicted values at any time. This aids in making decisions, keeping variations into account.

Forecasting use cases

Organizations need to check key driving metrics, such as for inventory demand, operational or customer support workload, customer footfalls, or sales/revenue/cost, before making any business decisions.

Save the time and cost of investing in a data science team by using OCI Forecasting. Measure simple or complex relationships among variables to accurately hit your target metrics over time and future-proof business decisions.

There are many examples of how vertical markets use forecasting to take a lead over competition, such as in:

• Banking and finance: bank deposits and withdrawal on interest rate changes, ATM cash management, or credit card transactions on offers
• Insurance: demands due to pandemic, insurance claims
• Retail: Sales based on promotions, price changes or weather conditions
• Supply chain and logistics: effective transportation planning, demand forecasting for effective manufacturing
• Information technology: Cloud or server capacity, web traffic
• Energy: Electric loads, price forecasting, wind and solar power forecasting

Predicting trends has always helped businesses make the right calls in a competitive environment. Now, machine learning-driven forecasting is improvising the accuracy of these predictions when compared to the limitations of earlier statistical methods.

OCI Forecasting is built on the insights gained from collaboration across many industry verticals. How will you apply OCI forecasting service to your business? Watch this space as we share your success stories.