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Oct 17, 2019   |  By Pratap Dangeti
Anomaly detection of time series can be solved in multiple ways. One of the methods is using deep learning-based autoencoder models utilizing encoder-decoder architecture. Before we deep-dive into the methodology in detail, here we are discussing the high-level flow of anomaly detection of time series using autoencoder models From the above mentioned all steps, our area of interest would be the development and application of deep learning-based autoencoder model step.
Oct 9, 2019   |  By Nithin Gangadharan
It’s a Friday evening and ABC Corporation call center is running on a lean roster. Friday evenings are generally a good time as the call velocity is less and types of inquiries coming in are basic that even the roster is planned in a such a way that your best resources are not in action. But things are about to change as the Infrastructure team is applying a minor patch in production which has been done many times, and this time has been picked due to low transaction volume at 8pm.
Sep 5, 2019   |  By Debolina Ray
The payment system saw a revolution from 2013. Slowly, but swiftly there were various payment concepts circulating and infiltrating the markets. E-commerce, of course, propelled the need for alternate payment methods from cash/cheque/draft onto a credit card, bank transfer, and e-wallets. There were concerns raised around privacy, interoperability, and security – which had quite a few of these ideas dwindled away.
Aug 6, 2019   |  By Kumar S
Anomaly detection is about identifying outliers in a time series data using mathematical models, correlating it various influencing factors and delivering insights to business decision makers. Using anomaly detection across multiple variables and automatically correlating it among them has significant potential to increase sales and margins for a typical ecommerce business.
Jul 9, 2019   |  By Rohit Maheshwari
Touted as the biggest sporting extravaganza in Indian cricket, the IPL season is famous not just for the tight finishes and action on the screens, but also for the action off it—for example the spike in online food orders. For instance, Swiggy reported a surge of around five to seven percent in orders during IPL season, compared to the other months (according to recent reports). Clearly, viewers did not want to miss out on any of the action, choosing to order their favorite delicacies.