Predictive Analytics

Less noise, better decisions.

Time Series Forecasting

Historical data can opens doors to improved future performance. Time Series Forecasting is the key.

Using your historical data, SPAR helps you improve forecasting and scientific prediction. We build models that offer insights into the ‘whys’ behind current outcomes so you can make better strategic decisions now and down the line.

Data integrity and quality (making sure the data is reliable, accurate, complete, and within context) are of the utmost importance for accurate forecasting. SPAR’s data engineering expertise ensures data quality and integrity to enable more precise and dynamic forecasts over extended time frames.

Want to see it in action? Contact us today.

Time Series Forecasting

Recommendation Systems

Recommendation Systems

Businesses use Recommender Systems to improve personalization, increase engagement, and make more sales. SPAR builds Content-based recommendation systems that are similar to user-user and item-item algorithms but the similarities are computed using only content-based features, (i.e. information extracted from raw text descriptions or images, using Computer Vision or NLP).
We also leverage Hybrid models and deep learning when building systems for clients. These models combine content-based models and deep learning which allows the system to capture granular details regarding the interactions between users and items.
A key benefit of our systems is that they don’t need gargantuan data sets or expensive servers to train their engines.

If your customers buy products from you, an intelligent recommender system will boost your bottom line significantly. How significant? Get in touch with us and we’ll demonstrate what you stand to earn.

Anomaly Detection

Organizations can preempt problems by catching anamolies as they happen. SPAR’s AI Anomaly Detection helps you catch anamolies in real time, automate alerts, and make detection and analysis more manageable.
Not all violations are problems and not every problem is a serious one.SPAR’s AI Anomoly Detection identifies abnormal behavior (anything that deviates from an established baseline pattern) so you can address it immediately. Our AI-driven solution autogenerates baselines, detects anomalies, remediates root cause, and sends alerts.

Regardless of your IT environment (on premises, multi-cloud, containers/microservices), our solution accurately categorizes anomalies according to their severity. This way you know what to address when according to its potential impact. The result: less noise, more problem solving Want to see what intelligent anomaly detection powered by AI can do for you? Ask for a free demo, today.

Anomaly Detection

Regression and Classification

Regression and Classification

If you need to improve how you predict and forecast continuous values (e.g. price, response rate) or discrete values (e.g. true/false, spam/not spam), we’ve got you covered.
Supervised learning algorithms like regression and classification enable prediction in machine learning. Say you have an opportunity to invest in a property, but aren’t sure about the property’s future value.
With regression, you can take your historical data, determine the variables that impact future value (e.g. square ft., location, school zone, etc.), find data for similar homes, and then train a model to make predictions that help you make better investment decisions. With classification, we build and train models to classify variables according to key parameters that then take suitable action. (e.g identifying whether or not an email is spam and then moving it to the appropriate folder). Interested, but not sure how you can leverage regression and classification to improve profitability? Contact us for a demo or to build a relevant proof-of-concept, today

Trusted Across Industries