Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks. You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima. The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands -On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more. What You'll Learn:* Explains basics to advanced concepts of time series * How to design, develop, train, and validate time-series methodologies * What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results * Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series. * Univariate and multivariate problem solving using fbprophet. Who This Book Is For Data scientists, data analysts, financial analysts, and stock market researchers

It has been reported that many tested and established antibiotics does not perform well in general and sometimes it is totally ineffective in specific cases. This inactiveness is inhibited by presence of some unwanted proteins received from different origins. In the present book one of such enzymes named NDM-1, its behavior and hindrance characteristics in the working of certain group of antibiotics wiz. A Carbapenem antibiotic has been addressed thoroughly. The mechanism of hindrance to particular group of drugs especially Polymyxins and Tigecycline antibiotics has been very frequently observed. To alleviate this undesirable situation docking of possible drug compounds with diseased proteins has been exhibited. Further, out of possible drug compounds best drug compound is identified after docking exercise of drugs over NDM-1. The best performing drug has been identified as CID 6249, Stock1n-13321 and CID 2174. The sole purpose of writing this book is to encourage similar researches. So as the solution of ineffectiveness of established, very important and life saving antibiotics may be obtained by and large in wide spectrum.

India is bestowed with rich water resources; rainfall is one of the main sources of water. Because of time and spatial variability of rainfall, a number of dams have been constructed all over the country to tap the available water resources so that this water can be utilized in accordance with the requirements of mankind. Proper management of the reservoirs is required for the efficient use of available water resources. Reservoir operation plays a vital role in planning and management of water resources system. Modeling of a rainfall-runoff is gaining a fast momentum for hydrological and water management studies. This evolution provides the mankind with new possibilities to efficiently use the available water. In this study rainfall-runoff model for three reservoirs, namely Ravishankar Sagar, Murumsilli and Dudhawa reservoirs of MRP Project has been developed, for the available data of 17 years, by the application of fuzzy logic. Fuzzified runoff results from the model are compared with linear regression by calculating the relative error with reference to the observed runoff. It has been found that fuzzy logic based rainfall-runoff model performs better than linear regression model