Click the "Learn Visuals" above, and select to add "Star" or "Pin to Top". Important resources delivered to you first. Author on Zhihu | masterSu Link | https://zhuanlan.zhihu.com/p/139617364 This article is about 3200 words, recommended reading time 5 minutes This article introduces the visualization of the LSTM model structure. Recently, I have been learning about … Read more
Click the card above to follow the “Heart of Time Series” public account A wealth of valuable content delivered instantly Diffusion-TS: Interpretable Diffusion for General Time Series Generation Introduction Time series data is ubiquitous in various fields such as finance, healthcare, retail, and climate modeling. However, data sharing can lead to privacy breaches, limiting the … Read more
The WeChat public account on quantitative investment and machine learning is a mainstream self-media in the industry, focusing on fields such as quantitative investment, hedge funds, Fintech, artificial intelligence, and big data. The public account has over 300K+ followers from industries such as public offerings, private equity, securities, futures, banks, insurance, and universities, and has … Read more
Importance of Neural Networks in Mathematical Competitions In mathematical competitions, neural networks have gradually become a powerful assistant for participants due to their strong data processing and pattern recognition capabilities. Especially when dealing with complex, nonlinear, high-dimensional data, neural networks perform exceptionally well. They not only help us solve basic problems such as classification and … Read more
In chemical plants, various process equipment are usually connected by pipelines. Therefore, pipelines are known as the “veins” of the chemical plant, serving the role of transporting various process media. The damage to pipelines during operation is often caused by corrosion. According to statistics, the losses caused by metal corrosion in China exceed 40 billion … Read more
Source: DeepHub IMBA This article is about 6800 words, and it is recommended to read for 10+minutes This article will discuss the techniques to obtain tangible value from the dataset using hypothesis testing, feature engineering, and time series modeling methods. Using statistical tests and machine learning to analyze and predict the performance of solar power … Read more
XGBoost and LightGBM are currently very popular tree-based machine learning models, both demonstrating efficient performance. However, they have different characteristics in certain situations. Simple Comparison of XGBoost and LightGBM Training Speed LightGBM has a significant advantage over XGBoost in terms of training speed. This is because LightGBM uses some efficient algorithms and data structures, such … Read more
Author: Jason Brownlee Translation: wwl Proofreader: Wang Yutong This article contains about 3300 words, and is recommended to be read in 10minutes This article introduces how to use XGBoost for time series forecasting, including transforming time series into a supervised learning prediction problem, using forward validation for model evaluation, and providing actionable code examples. For … Read more
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import xgboost as xgb from sklearn.metrics import mean_squared_error color_pal = sns.color_palette() plt.style.use('fivethirtyeight') df = pd.read_csv('AAPL Hourly.csv') df = df[['timestamp', 'close']] df = df.set_index('timestamp') df.plot(style='.', figsize=(15, 5), color=color_pal[0], title='stock growth') plt.show() df.head() mean = df['close'].mean() mean 162.3033402402023 df.index = pd.to_datetime(df.index) … Read more
Source: DeepHub IMBA This article is about 3300 words, suggested reading time is 10 minutes. This article will introduce the BiTCN model, which utilizes two Temporal Convolutional Networks (TCN) to encode past and future covariates while maintaining computational efficiency. In the field of time series forecasting, model architecture often relies on Multi-Layer Perceptron (MLP) or … Read more