Abstract
With plethora of data points available at organization’s disposal, it becomes difficult to understand how to decipher trends hidden within these data points. Every sequence of data tends to follow a certain trend. This data can be revenue numbers, conversions, purchases, logins, anything, and everything. As a marketer if we are able to identify this pattern, it can help us accomplish a lot. If we can find out these trends, we shall be able to predict, with a certain degree of accuracy, about what is going to happen in future, for example, what is going to be the revenue that we shall earn in next three months, what would be the sales in next one year, so on and so forth. The question at this point is how exactly we can find these trends hidden in plain sight. This is where mathematics in conjunction with machine learning comes to the rescue. This paper answers two key questions: (1) How to identify the patterns of your observations? (2) How to utilize this pattern to predict for future? As a prerequisite we would require observations that are collected over equal intervals of time for some specific dimension/category like monthly prices of a specific commodity, weekly revenue earned, and monthly sales by volume, etc. In this paper, we have talked about the importance of machine learning in forecasting using time series and validated it by numerical illustration.
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Mehta, K., Singhal, E., Shrivastava, A.K. (2020). Time Series Analysis: A Machine Learning Approach. In: Kapur, P.K., Singh, G., Klochkov, Y.S., Kumar, U. (eds) Decision Analytics Applications in Industry. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-15-3643-4_14
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