Abstract
With the rapid expansion of e-commerce businesses shortest delivery time is a challenging task. As well as price and quality, delivery time is becoming an important key-factor in the race of business growth. To deliver the product to the buyer in the shortest time, the product should be at the local inventory at the time of purchasing. Which is a difficult task for e-commerce companies to store the right products at right time in local inventories. Extracting useful patterns and forecasting demand from purchase history is a tough job because of the huge volume and high variety nature of e-commerce data. The MapReduce algorithm of Big Data ecosystem can be used to pre-process the enormous amount of data and to summarize it into a suitable format which later can easily be used to build any forecasting model. A comparison of different classical time series forecasting methods with the pre-processing of MapReduce algorithm is focused on this work.
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