A State-of-the-Art Review of Time Series Forecasting Using Deep Learning Approaches

Time series forecasting has recently emerged as a crucial study area with a wide spectrum of real-world applications. The complexity of data processing originates from the amount of data processed in the digital world. Despite a long history of successful time-series research using classic statistical methodologies, there are some limits in dealing with an enormous amount of data and non-linearity. Deep learning techniques effectually handle the complicated nature of time series data. The effective analysis of deep learning approaches like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long short-term memory (LSTM), Gated Recurrent Unit (GRU), Autoencoders, and other techniques like attention mechanism, transfer learning, and dimensionality reduction are discussed with their merits and limitations. The performance evaluation metrics used to validate the model's accuracy are discussed. This paper reviews various time series applications using deep learning approaches with their benefits, challenges, and opportunities

Deep Learning based Load Forecasting with Decomposition and Feature Selection Techniques

The forecasting of short term electricity load plays a vital role in power system. It is essential for the power system's reliable, secure, and cost-effective functioning. This paper contributes significantly for enhancing the accuracy of short term electricity load forecasting. It presents a hybrid forecasting model called Gated Recurrent Unit with Ensemble Empirical Mode Decomposition and Boruta feature selection (EBGRU). It is a hybrid model that addresses the non-stationary, non-linearity and noisy issues of the time series input by using Ensemble Empirical Mode Decomposition (EEMD). It also addresses overfitting and curse of dimensionality issues of load forecasting by identifying the pertinent features using Boruta wrapper feature selection. It effectively handles the uncertainty and temporal dependency characteristics of load and forecasts the future load using deep learning based Gated Recurrent Unit (GRU). The proposed EBGRU model is experimented by using European and Australian Electricity load datasets. The temperature has high correlation with load demand. In this study, both load and temperature features are considered for the accurate short term load forecasting. The experimental outcome demonstrates that the proposed EBGRU model outperforms other deep learning models such as RNN, LSTM, GRU, RNN with EEMD and Boruta (EBRNN) and LSTM with EEMD and Boruta (EBLSTM)

Morphological and molecular characterization of Aedes aegypti variant collected from Tamil Nadu, India

Background & objectives: Accurate mosquito species identification is the basis of entomological surveys and effective vector control. Mosquito identification is either done morphologically using diagnostic features mentioned in taxonomic keys or by molecular methods using cytochrome oxidase subunit 1 (coxI) and Internal transcribed spacer 2 (ITS2). Methods: We performed a larval survey for Aedes mosquitoes from eight different geographical regions in Tamil Nadu, India. The mosquitoes collected during the survey were characterized using both morphological and molecular markers. Results: During an entomological survey from eight different geographical regions in Southern India, a morphological variety named Aedes aegypti var. luciensis was observed. The variant mosquitoes were characterized using both morphological and molecular markers. The variant mosquitoes differed only in the dark scaling of 5th segment of hind-tarsi. Around one third to two third of the 5th segment in variant mosquitoes was dark which has been described as white in identification keys. No other significant difference was observed in adults or immature stages. The variation was heritable and coexisting in the field with the type form mosquitoes. Comparison of the genetic profile of coxI and ITS2 were similar in variant and the type form indicating both of them to be conspecific. Interpretation & conclusion: The morphological variant mosquitoes were found genetically similar to the Ae. aegypti type form. However, considering its high prevalence and coexistence with Ae. aegypti type form in different geographical regions, detailed studies on bionomics, ecology, genetics, behavior as well as its plausible role in disease transmission are warranted