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所在平台: Udemy |
课程主页: https://www.udemy.com/course/applied-time-series-analysis-in-python/
课程评论：没有评论
课程名称：Python应用时间序列分析 概述：该课程是唯一结合最新统计方法和深度学习技术的时间序列分析课程。课程首先涵盖时间序列的基本概念，如平稳性和增广迪基-福勒检验、季节性、白噪声、随机游走、自回归、移动平均、ACF和PACF，以及使用赤池信息量准则（AIC）进行模型选择。 随后，课程将深入探讨更复杂的统计模型以进行时间序列预测，包括ARIMA（自回归积分移动平均模型）、SARIMA（季节性自回归积分移动平均模型）和SARIMAX（带外生变量的季节性自回归积分移动平均模型）。此外，还将介绍多个时间序列预测的方法，包括VAR（向量自回归）、VARMA（向量自回归移动平均模型）和VARMAX（带外生变量的向量自回归移动平均模型）。 接下来，课程将进入深度学习部分，使用Tensorflow应用不同的深度学习技术进行时间序列分析，包括简单线性模型（1层神经网络）、深度神经网络（DNN）、卷积神经网络（CNN）、长短期记忆网络（LSTM）、CNN与LSTM模型结合以及残差网络（ResNet）和自回归LSTM。 在整个课程中，学员将完成超过5个端到端的Python项目，并可获得所有源代码。
This is the only course that combines the latest statistical and deep learning techniques for time series analysis. First, the course covers the basic concepts of time series:stationarity and augmented Dicker-Fuller testseasonalitywhite noiserandom walkautoregressionmoving averageACF and PACF,Model selection with AIC (Akaike's Information Criterion)Then, we move on and apply more complex statistical models for time series forecasting:ARIMA (Autoregressive Integrated Moving Average model)SARIMA (Seasonal Autoregressive Integrated Moving Average model)SARIMAX (Seasonal Autoregressive Integrated Moving Average model with exogenous variables)We also cover multiple time series forecasting with:VAR (Vector Autoregression)VARMA (Vector Autoregressive Moving Average model)VARMAX (Vector Autoregressive Moving Average model with exogenous variable)Then, we move on to the deep learning section, where we will use Tensorflow to apply different deep learning techniques for times series analysis:Simple linear model (1 layer neural network)DNN (Deep Neural Network)CNN (Convolutional Neural Network)LSTM (Long Short-Term Memory)CNN + LSTM modelsResNet (Residual Networks)Autoregressive LSTMThroughout the course, you will complete more than 5 end-to-end projects in Python, with all source code available to you.
