Time series data is prevalent across numerous fields, necessitating the development of robust and accurate forecasting models. Capturing patterns both within and between temporal and multivariate components is crucial for reliable predictions. We introduce xLSTM-Mixer, a model designed to effectively integr...
xLSTMTime paper:https://arxiv.org/pdf/2407.10240 xLSTMTime for time series forecasting Abstract: In recent years, transformer-based models have gained prominence in multivariate long-term time series forecasting (LTSF), demonstrating significant advancements despite facing challenges such as high computa...
[LG]《xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories》M Kraus, F Divo, D S Dhami, K Kersting [U Darmstadt] (2024) http://t.cn/A6n2PNnz #机器学习##人工智能##论文#
This makes sLSTM potentially more robust and capable in applications like language modeling and time series forecasting, where understanding context and maintaining it over time is crucial. These innovations in memory mixing help address some of the inherent limitations of traditional RNNs and LSTMs,...
In recent years, transformer-based models have gained prominence in multivariate long-term time series forecasting (LTSF), demonstrating significant advancements despite facing challenges such as high computational demands, difficulty in capturing temporal dynamics, and managing long-term dependencies. The em...
我跟随这篇教程https://www.youtube.com/watch?v=QIUxPv5PJOY来预测未来某一天苹果的股价。代码是: #Import the libraries import math import pandas_datareader as web import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler ...
Time series forecastingElectricity demandNowadays, electricity is a basic commodity necessary for the well-being of any modern society. Due to the growth in electricity consumption in recent years, mainly in large cities, electricity forecasting is key to the management of an efficient, sustainable ...
and a single LSTM were adopted as comparative forecast models. The results indicated that the VMD-LSTM model had the best forecasting performance among all the models in terms of its Nash-Sutcliffe error (NSE=0.930), root mean square error (RMSE=0.385), and coefficient of determination (R2=0....
Specifically, spatial and temporal correlations are explicitly modeled and respectively maintained in cells to capture the complex non-linear patterns in correlated time series. A general interface for handling external factors is further designed to enhance forecasting performance of the model. Experiments...
Temperature prediction using fuzzy time series. Systems, Man, and Cybernetics, Part B: Cybernetics IEEE Transactions on, 30 (2) (2000), pp. 263-275 View in ScopusGoogle Scholar 6 Maqsood I., M.R. Khan, A. Abraham An ensemble of neural networks for weather forecasting Neural Computing & ...