Benchmarks
Greatest papers with code
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.
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.
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.
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.
LFRT, A MATLAB Toolbox for Load Forecast & Assesment of Additional Capacity for an Electrical Power System
A developing country like Pakistan with sizable pressure on their limited financial resources can ill afford either of these two situations about energy forecast: 1) Too optimistic 2) Too conservative.
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.
Short-term forecasting of Amazon rainforest fires based on ensemble decomposition model
Accurate forecasting is important for decision-makers.