Jaydip Sen

A Proposed Architecture for Big Data Driven Supply Chain Analytics

Advancement in information and communication technology (ICT) has given rise to explosion of data in every field of operations. Working with the enormous volume of data (or Big Data, as it is popularly known as) for extraction of useful information to support decision making is one of the sources of competitive advantage for organizations today. Enterprises are leveraging the power of analytics in formulating business strategy in every facet of their operations to mitigate business risk. Volatile global market scenario has compelled the organizations to redefine their supply chain management (SCM). In this paper, we have delineated the relevance of Big Data and its importance in managing end to end supply chains for achieving business excellence. A Big Data-centric architecture for SCM has been proposed that exploits the current state of the art technology of data management, analytics and visualization. The security and privacy requirements of a Big Data system have also been highlighted and several mechanisms have been discussed to implement these features in a real world Big Data system deployment in the context of SCM. Some future scope of work has also been pointed out.

Decomposition of Time Series Data to Check Consistency between Fund Style and Actual Fund Composition of Mutual Funds

We propose a novel approach for analysis of the composition of an equity mutual fund based on the time series decomposition of the price movements of the individual stocks of the fund. The proposed scheme can be applied to check whether the style proclaimed for a mutual fund actually matches with the fund composition. We have applied our proposed framework on eight well known mutual funds of varying styles in the Indian financial market to check the consistency between their fund style and actual fund composition, and have obtained extensive results from our experiments. A detailed analysis of the results has shown that while in majority of the cases the actual allocations of funds are consistent with the corresponding fund styles, there have been some notable deviations too.

A Framework of Predictive Analysis of Tourist Inflow in the Beaches of West Bengal: A Study of Digha-Mandarmoni Beach

Tourism is increasingly becoming an extremely important sector with its rapidly increasing contribution to GDP of any state or country as a whole. Analyzing and predicting tourist inflow not only enables us to make an accurate estimate of the number of tourists that is likely to visit a destination, but it also provides us with an opportunity to gear up the capacities of that place in terms of logistics, hospitality etc. in order to cater to the tourists leading to an overall socio economic development of the place. This paper presents a study on the tourism demand for two very popular beaches of the state of West Bengal in India. In this work, time series values of the domestic tourist inflow to Digha and Mandarmoni beaches in West Bengal are used for the period of January 2008–December 2014. The time series is decomposed into its components – trend, seasonal, and random – in order to make further analysis. Based on the structural analysis, five different approaches of forecasting are formulated and the forecast accuracy is computed for each of the methods. Using R statistical tool, extensive results have been presented that provide very meaningful insights to the tourists’ inflow time series. The results also demonstrate the effectiveness of our proposed forecasting framework.

A Predictive Analysis of the Indian FMCG Sector Using Time Series Decomposition-Based Approach

Abstract. Stock price movements being random in its nature, prediction of stock prices using time series analysis presents a very difficult and challenging problem to the research community. However, over the last decade, due to rapid development and evolution of sophisticated algorithms for complex statistical analysis of large volume of time series data, and availability of high-performance hardware and parallel computing architecture, it has become possible to efficiently process and effectively analyze voluminous and highly diverse stock market time series data effectively, in real-time. Robust predictive models are being built for accurate forecasting of values of highly random variables such as stock price movements. This paper has presented a highly reliable and accurate forecasting framework for predicting the time series index values of the fast moving consumer goods (FMCG) sector in India. A time series decomposition approach is followed to understand the behavior of the FMCG sector time series for the period January 2010 till December 2016. Based on the structural analysis of the time series, six methods of forecast are designed. These methods are applied to predict the time series index values for the months of 2016. Extensive results are presented to demonstrate the effectiveness ofthe proposed decomposition approaches of time series and the efficiency of the six forecasting methods. Keywords. Time series decomposition, Trend, Seasonal, Random, Holt Winters Forecasting model, Auto Regression (AR), Moving Average (MA), Auto Regressive Integrated Moving Average (ARIMA), Partial Auto Correlation Function (PACF), Auto Correlation Function (ACF). JEL. G11, G14, G17, C63.