Benchmarks
Latest papers with code
Data-Driven Copy-Paste Imputation for Energy Time Series
The CPI method copies data blocks with similar properties and pastes them into gaps of the time series while preserving the total energy of each gap.
Probabilistic Load Forecasting Based on Adaptive Online Learning
Conventional load forecasting techniques obtain single-value load forecasts by exploiting consumption patterns of past load demand.
Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets
Electricity load forecasting enables the grid operators to optimally implement the smart grid's most essential features such as demand response and energy efficiency.
Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)
The COVID-19 pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread.
EMOTION RECOGNITION INFORMATION RETRIEVAL LOAD FORECASTING MISINFORMATION NAMED ENTITY RECOGNITION QUESTION ANSWERING
Short-term forecasting of Amazon rainforest fires based on ensemble decomposition model
Accurate forecasting is important for decision-makers.
Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model
A novel bioinspired metaheuristic is proposed in this work, simulating how the coronavirus spreads and infects healthy people.
Deep Learning for Time Series Forecasting: The Electric Load Case
Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remains a challenging task.
Deep Adaptive Input Normalization for Time Series Forecasting
• passalis/dain
• 
Deep Learning (DL) models can be used to tackle time series analysis tasks with great success.
LOAD FORECASTING TIME SERIES TIME SERIES ANALYSIS TIME SERIES FORECASTING
Short-term Load Forecasting with Deep Residual Networks
We present in this paper a model for forecasting short-term power loads based on deep residual networks.
Fast and Accurate Time Series Classification with WEASEL
• patrickzib/SFA
• 
On the popular UCR benchmark of 85 TS datasets, WEASEL is more accurate than the best current non-ensemble algorithms at orders-of-magnitude lower classification and training times, and it is almost as accurate as ensemble classifiers, whose computational complexity makes them inapplicable even for mid-size datasets.