A Solution to Forecast Demand Using Long Short-Term Memory Recurrent Neural Networks for Time Series Forecasting
Adarsh Goyal
Purdue University
Adarsh received his Integrated Master’s degree in Geophysical Technology from Indian Institute of Technology (IIT) Roorkee in 2017. The course structure of the program inspired his interest in statistics and familiarized him with several advanced level courses in mathematics. He joined MS in Business Analytics program at Purdue immediately after graduation. Adarsh improved his understanding of business fundamentals and developed his skill sets in predictive analytics, optimization, data mining, and machine learning through hands-on experience while working on industry projects. Going forward, he sees himself in data scientist/analyst roles working on various data heavy assignments to solve challenging business problems.
Ruthwik Kumar
Purdue University
Ruthwik hails from Mumbai, India. He is currently pursuing his Master’s in Business Analytics and Information Management from Purdue University.Prior to joining the program, he worked as a decision analytics associate at ZS Associates, India, gaining analytical experience with projects on the global pharmaceutical industry using Forecasting, Key-Driver Analytics, Targeting, Alignment, Sizing and Decision Sciences on tools like Excel, R, Python, VBA, SAS, SQL with Oracle, and Tableau.Going forward, Ruthwik aims to build specialization in Predictive Analytics and Data Mining, leveraging it to drive business strategy and performance for leading consulting firms, enabling effective decision-making.
Shubhda Kulkarni
Purdue University
Shubhda graduated from National Institute of Food Technology, Entrepreneurship and Management, Haryana in India in 2017 with a Bachelor of Technology degree. She is pursuing Master of Sciences in Business Analytics and Information Management, she intends to use numbers to facilitate data-driven decision making to cater to real world business problems. Two to three decades in the future, she wants to work for the betterment of the society and use her analytical knowledge in the areas of food sufficiency, disaster management and other common catastrophes in the world.
Shreyas Krishnamurthy
Purdue University
Shreyas is a Master’s candidate in Business Analytics at the Krannert School of Management, Purdue University. He also has a Master’s Degree in Commerce from the University of Mumbai and has worked at Deloitte for three years as part of their Risk Advisory Vertical. As a risk consultant, Shreyas worked with multiple clients across various industries such as manufacturing, hospitality, service, chemical and financial services. Shreyas believes that a Master’s in Business Analytics will help him communicate complex data to the client in an attractive and simplified manner, which he believes is the key to success for any consultant.
Madhurima Vartak
Purdue University
Madhurima graduated with a Bachelor’s in Electronics and Tele-Communications from Sardar Patel Institute of Technology, Mumbai. Immediately after graduation, she joined Deloitte as an Advisory Analyst, where she worked with the Internal Audit team to help clients gain a greater understanding of the risks, controls, and governance related to evolving IT systems, application, and technologies.After graduating from Purdue, Madhurima looks forward to being proficient in advanced analytical modeling methods and visualization tools, so that she can provide optimized business solutions to her clients. She also aims to solve equally challenging social problems by applying such techniques.
Abstract
This study focuses on predicting demand based on data collected across many periods. To help our client build a solution to forecast demand effectively, we developed Long Short-Term Memory (LSTM) Networks model, a type of... [ view full abstract ]
Authors
- Adarsh Goyal (Purdue University)
- Ruthwik Kumar (Purdue University)
- Shubhda Kulkarni (Purdue University)
- Shreyas Krishnamurthy (Purdue University)
- Madhurima Vartak (Purdue University)
Topic Area
Topics: Topic #1
Session
SPP-2 » Student Presentations & Posters (10:45 - Friday, 13th April, Haymarket Station B)
Paper
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