Abstract-Time Series data is large in volume, highly dimensional and continuous updating. Time series data analysis for forecasting, is one of the most important aspects of the practical usage. Accurate rainfall forecasting with the help of time series data analysis will help in evaluating drought and flooding situations in advance. In this paper, Artificial Neural Network (ANN) technique has been used to develop one-month and twomonth ahead forecasting models for rainfall prediction using monthly rainfall data of Northern India. In these model, Feed Forward Neural Network (FFNN) using Back Propagation Algorithm and Levenberg-Marquardt training function has been used. The performance of both the models has been assessed based on Regression Analysis, Mean Square Error (MSE) and Magnitude of Relative Error (MRE). Proposed ANN model showed optimistic results for both the models for forecasting and found one month ahead forecasting model perform better than two months ahead forecasting model. This paper also gives some future directions for rainfall prediction and time series data analysis research.

Abstract. Time series data available in huge amounts can be used in decisionmaking. Such time series data can be converted into information to be used for forecasting. Various techniques are available for prediction and forecasting on the basis of time series data. Presently, the use of data mining techniques for this purpose is increasing day by day. In the present study, a comprehensive survey of data mining approaches and statistical techniques for rainfall prediction on time series data was conducted. A detailed comparison of different relevant techniques was also conducted and some plausible solutions are suggested for efficient time series data mining techniques for future algorithms.

Background: The coronavirus disease-2019 (COVID-19) pandemic has disrupted management of non-COVID-19 illnesses, including cancer. For many solid organ cancers, surgical intervention is imperative. We present our experience with major operations during a nationwide lockdown. Method: This was an observational study of 184 patients, analyzing their perioperative outcomes and categorizing morbidity according to Clavien-Dindo Classification. Strict screening required symptomatic patients to be referred to COVID centers and their operations postponed. Continuous and categorical variables were

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