In the era of modern technology, the competitive paradigm among organisations is changing
at an unprecedented rate. New success measures are applied to the organisation's supply …

Objective: To examine the current trends in surgical-orthodontic treatment for patients with
Class III malocclusion using time-series analysis. Materials and Methods: The records of …

A time series modeling approach (Box-Jenkins' ARIMA model) has been used in this study
to forecast sugarcane production in India. The order of the best ARIMA model was found to …

Forecasting of future stock prices is a complex and challenging research problem due to the
random variations that the time series of these variables exhibit. In this work, we study the …

Time series statistical analyses (TSSA) have been employed to evaluate the variability of
resistive switching memories and to model the set and reset voltages for modeling purposes …

One of the challenging research problems in the domain of time series analysis and
forecasting is making efficient and robust prediction of stock market prices. With rapid …

Designing efficient and robust algorithms for accurate prediction of stock market prices is
one of the most exciting challenges in the field of time series analysis and forecasting. With …

Time series analysis and forecasting of stock market prices has been a very active area of
research over the last two decades. Availability of extremely fast and parallel architecture of …

The objective of this work is to ascertain sectoral characteristics of stock market indices of
India through time series decomposition. It is postulated that different sectors in an economy …

Inaccurate forecasts can cause severe financial consequences and disrupt supply chain
operations for organisations. This study focuses on the pharmaceutical industry, renowned …