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  Many business executives complain that the top pain point to them is the sudden negative variations in the demand of the product. Changes that no one saw it coming! The world’s largest IT research firm, Gartner came up with a term for the same problem that many organizations suffer from : Demand Volatility.    There are numerous factors that affect the demand of a particular product or service, be it weather fluctuations, trends, availability of alternatives, period of recession and looking at the power of Social media- even posts by Social influencers or celebrities impact the buyers.     For instance, let's look at both sides of the coin here.    Daniel Wellington, a Swedish watch startup, is one company that uses a global network of influencers to market their watches. One of the influencers on Instagram posted an eye-catching and well-composed image with a caption that not only talked about the watch in photo but also how her followers could get one with a discount. Due to it

Multivariate Time Series Forecasting is an important problem in many domains. Be it forecasting the demand for a product, or finding weather patterns, using time series parameters from the present to predict the future is vital to many organisations. In this article we are going to see how to use recurrent neural networks with convolutional neural networks to predict time series forecast for grocery store sales. The data can be found here , and the code can be found here . This implementation was developed as a prototype for AI Hello, a company based in Toronto that provides e-commerce solutions including sales prediction for sellers on e-commerce platforms such as Amazon, Woocommerce , and Shopify. We are using an implementation called LSTNet to perform this prediction. LSTNet uses convolutional networks and recurrent networks in conjunction. Additionally, it also provides the capability to detect long or short term patterns according to the nature of the data. Data Preparation and