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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/324889271

An Introduction to Time Series Forecasting with Python

Presentation · April 2018

DOI: 10.13140/RG.2.2.18053.86249

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Andrii Gakhov

V. N. Karazin Kharkiv National University

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An Introduction toAn Introduction to

Time Series ForecastingTime Series Forecasting

with Pythonwith Python

Andrii Gakhov, ferret go GmbHAndrii Gakhov, ferret go GmbH

www.gakhov.comwww.gakhov.com

PyCon UA, Kharkiv, April 28-29, 2018PyCon UA, Kharkiv, April 28-29, 2018

What is a Time Series?What is a Time Series?

A sequential set of data pointsA sequential set of data points

measured over timemeasured over time..

- a variable that represents the elapsed time. - a variable that represents the elapsed time.

((

)),,

== 00,, 11,, 22,, 33,, .. .. ..

ExamplesExamples

hourlyhourly number of page views for a website number of page views for a website

dailydaily air temperature in a city air temperature in a city

monthlymonthly average number of reported bugs average number of reported bugs

quarterlyquarterly income for a company income for a company

annualannual population data in a country population data in a country

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achilles520

2020-05-04

一个破PPT竟然要14个积分，差评，就是一个支持向量机的代码

Time series forecasting using a hybrid ARIMA and neural network model. 这篇论文的更多信息

这篇论文的标题是"Time series forecasting using a hybrid ARIMA and neural network model"，也就是使用混合ARIMA和神经网络模型进行时间序列预测。作者可能是在探索如何提高时间序列预测的准确性和稳定性，因此使用了两种经典的方法进行组合。在这篇论文中，作者首先介绍了ARIMA模型和神经网络模型的基本原理，并讨论了它们的优点和缺点

Deep Time Series Forecasting with Python.pdf

Deep Time Series Forecasting with Python. 使用深度学习技术进行时间序列回归预测

下载Mastering Machine Learning with Python in Six Steps 英文高清完整.pdf 版

Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python By Manohar Swamynathan English | PDF | 2017 | 374 Pages | ISBN : 1484228

informer: beyond efficient transformer for long sequence time-series forecasting

### 回答1： informer是一种能够高效预测长时间序列的模型，它采用了超越传统transformer模型的设计和算法。相比传统transformer模型，informer模型在处理长序列时更加高效，同时能够保证预测的准确性。这种模型在处理时间序列预测问题时表现出色，是一种非常有前景的技术。 ### 回答2： Informer是一种新型的长序列时间序列预测模型，它具有比传统模型更高的

请将下列英文翻译成中文： Soil moisture is critical to agricultural business, ecosystem health, and certain hydrologically driven natural disasters. Monitoring data, though, is prone to instrumental noise, wide ranging extrema, and nonstationary response to rainfall where ground conditions change. Furthermore, existing soil moisture models generally forecast poorly for time periods greater than a few hours. To improve such forecasts, we introduce two data-driven models, the Naive Accumulative Representation (NAR) and the Additive Exponential Accumulative Representation (AEAR). Both of these models are rooted in deterministic, physically based hydrology, and we study their capabilities in forecasting soil moisture over time periods longer than a few hours. Learned model parameters represent the physically based unsaturated hydrological redistribution processes of gravity and suction. We validate our models using soil moisture and rainfall time series data collected from a steep gradient, post-wildfire site in southern California. Data analysis is complicated by rapid landscape change observed in steep, burned hillslopes in response to even small to moderate rain events. The proposed NAR and AEAR models are, in forecasting experiments, shown to be competitive with several established and state-of-the-art baselines.

土壤水分对农业业务、生态系统健康和某些水文驱动的自然灾害至关重要。然而，监测数据容易受到仪器噪声、极端宽泛和非平稳的降雨反应的影响，在地面条件发生变化时。此外，现有的土壤水分模型一般在几个小时以上的时间段内预测不佳。为了改善这样的预测，我们引入了两种数据驱动模型，即简单累积表示（NAR）和加性指数累积表示（AEAR）。这两个模型都根源于确定性的、基于物理的水文学，我们研究了它们在预测几个小时以上的

Financial_Risk_Forecasting

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构造完整的random_forecasting .py程序代码。运行带有随机森林分类器的代码，在输入参数中使用rf标志，通过运行以下命令: $ python random_forests.py——分类器类型rf 将两幅图(如果成功)保存到以下表格中，以便提交。现在，通过在输入参数中使用erf标志，使用极其随机的森林分类器运行代码。执行如下命令: $ python random_forests.py——分类器类型erf

我可以回答这个问题。以下是构造完整的random_forecasting.py程序代码： ``` import pandas as pd from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier from sklearn.metrics import accuracy_score from sklearn.

Wordle games are popular all over the world, and we have collected relevant data in the past year, we will analyze and process the relevant data to get valuable information, which will help wordle games gain more sustainable heat and life. In the first sub-question of question 1 we build a prediction model for the number of reports versus time using the common time-series analysis models ARIMA model and Holt model. The appropriate parameters of the ARIMA model are obtained based on the AIC information criterion, and the results are analyzed and compared after analyzing the best parameters of the Holt model using spss. We selected the test set forecasting accuracy, R-square, and root mean square error RMSE as indicators, and finally selected Holt model. The number of people reported on 2023-3-1 is 11733, and the prediction intervals at different confidence levels are given. For the second sub-question of question 1, we use word frequency to define word frequency, and use the crawled Wordle inputtable word list and combine it with Wordle game rules to define word similarity, and classify the similarity into similarity 1 and similarity 2, which correspond to yellow and green patterns respectively. The more words that meet the conditions, the higher the word similarity, and the lower the possibility of players filtering to the answer. When predicting the proportion of difficult mode, we still consider the temporal model, but at this time we divide the players into two types: faithful type and hot type, and the effect of hot type on the proportion of difficult mode is short-lived, so we consider the LSTM model with different treatments for the far and near future to predict the proportion of difficult mode on 2023-3-1. The final result was obtained as 9.86.

KaggleM5 Forecasting：传统预测方法与机器学习预测方法对比

本文的出发点在于比较传统预测方法和机器学习预测方法。本文使用的数据集来自 kaggle：M5 Forecasting — Accuracy。该数据集包含有 California、Texas、Wisconsin 三个州的产品类别、部门、仓储信息等。基于这些数据，需要预测接下来 28 天的每日销售量。涉及到的方法有：单指数平滑法双指数平滑法三指数平滑法 ARIMA SARIMA SARIM

给我一些用Recurrent Neural Network进行股票价格预测的高引用论文

以下是几篇使用循环神经网络（Recurrent Neural Network，RNN）进行股票价格预测的高引用论文： 1. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. 这篇经典论文提出了一种新的循环神经网络模型——长短时记忆网络（L

pytorch-forecasting

PyTorch-Forecasting是一个基于PyTorch的时间序列预测库，它提供了一系列的模型和工具，可以帮助用户进行时间序列的建模、预测和评估。该库支持多种时间序列预测任务，包括单变量和多变量预测、多步预测和分层预测等。同时，该库还提供了一些常用的时间序列预处理和后处理工具，如滑动窗口、差分、归一化等，以及可视化工具，方便用户进行数据分析和模型调试。

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time series forecasting with python , gakhov 高清

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Time series forecasting using a hybrid ARIMA and neural network model. 这篇论文的更多信息

Deep Time Series Forecasting with Python.pdf

下载Mastering Machine Learning with Python in Six Steps 英文高清完整.pdf 版

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时间序列分析 预测与控制 Time Series Analysis -- Forecasting and Control, 3rd Edition

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这篇论文 The Performance of LSTM and BiLSTM in Forecasting Time Series 主要内容

基于循环神经网络的股票收益率分析有什么文献可以参考吗

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KaggleM5 Forecasting：传统预测方法与机器学习预测方法对比

给我一些用Recurrent Neural Network进行股票价格预测的高引用论文

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时间序列分析预测与控制 Time Series Analysis -- Forecasting and Control, 3rd Edition