Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Deep learning framework to forecast electricity demand

A novel infectious coronavirus disease (COVID-19) identified in late 2019 has now been labelled as a global pandemic by World Health Organization (WHO). The COVID-19 outbreak has shown some positiv...

https://www.tandfonline.com/doi/pdf/10.1080/09603123.2020.1778646

Impact of lockdown measures during COVID-19 on air quality- A case study of India.

Electricity is of great significance for national economic, social, and technological activities, such as material production, healthcare, and education The nationwide electricity demand has grown rapidly over the past few decades Therefore, efficient electricity demand estimation and management are required for better strategies planning, energy utilization, waste management, improving revenue, and maintenance of power systems In this paper, we propose an empirical mode decomposition (EMD)-based deep learning approach which combines the EMD method with the long short-term memory network model to estimate electricity demand for the given season, day, and time interval of a day For this purpose, the EMD algorithm decomposes a load time series signal into several intrinsic mode functions (IMFs) and residual Then, a LSTM model is trained separately for each of the extracted IMFs and residual Finally, the prediction results of all IMFs are combined by summation to determine an aggregated output for electricity demand To demonstrate the applicability of the proposed approach, it is applied to electricity consumption data of city Chandigarh Furthermore, the performance of the proposed approach is evaluated by comparing the prediction results with recurrent neural network (RNN), LSTM, and EMD-based RNN (EMD+RNN) models

Empirical Mode Decomposition Based Deep Learning for Electricity Demand Forecasting

In present study, the variation in concentration of key air pollutants such as PM 2.5, PM 10, NO 2, SO 2 and O 3 during the pre-lockdown and post-lockdown phase has been investigated. In addition, the monthly concentration of air pollutants in March, April and May of 2020 is also compared with that of 2019 to unfold the effect of restricted emissions under similar meteorological conditions. To evaluate the global impact of COVID-19 on the air quality, ground-based data from 162 monitoring stations from 12 cities across the globe are analysed for the first time. The concentration of PM 2.5, PM 10 and NO 2 were reduced by 20-34%, 24-47% and 32-64%, respectively, due to restriction on anthropogenic emission sources during lockdown. However, a lower reduction in SO 2 was observed due to functional power plants. O 3 concentration was found to be increased due to the declined emission of NO. Nevertheless, the achieved improvements were temporary as the pollution level has gone up again in cities where lockdown was lifted. The study might assist the environmentalist, government and policymakers to curb down the air pollution in future by implementing the strategic lockdowns at the pollution hotspots with minimal economic loss.

Durga Toshniwal

Papers

Deep learning framework to forecast electricity demand

Impact of lockdown measures during COVID-19 on air quality- A case study of India.

Empirical Mode Decomposition Based Deep Learning for Electricity Demand Forecasting

Impact of lockdown on air quality over major cities across the globe during COVID-19 pandemic.

Deep learning models for solar irradiance forecasting: A comprehensive review