Avril Lavigne - Innocence

nice

 

(Avril Lavigne, Evan Taubenfeld)
Published by Avril Lavigne Publishing Ltd./Almo Music Corp. (ASCAP), Big Evil Music Co./EMI April Music, Inc. (ASCAP)

Waking up I see that everything is ok
The first time in my life and now it's so great
Slowing down I look around and I am so amazed
I think about the little things that make life great
I wouldn't change a thing about it
This is the best feeling

This innocence is brilliant, I hope that it will stay
This moment is perfect, please don't go away, I need you now
And I'll hold on to it, don't you let it pass you by

I found a place so safe, not a single tear
The first time in my life and now it's so clear
Feel calm I belong, I'm so happy here
It's so strong and now I let myself be sincere
I wouldn't change a thing about it
This is the best feeling

This innocence is brilliant, I hope that it will stay
This moment is perfect, please don't go away, I need you now
And I'll hold on to it, don't you let it pass you by

It's the state of bliss you think you're dreaming
It's the happiness inside that you're feeling
It's so beautiful it makes you wanna cry

It's the state of bliss you think you're dreaming
It's the happiness inside that you're feeling
It's so beautiful it makes you wanna cry

It's so beautiful it makes you want to cry

This innocence is brilliant, it makes you want to cry
This innocence is brilliance, please don't go away
Cause I need you now
And I'll hold on to it, don't you let it pass you by

This innocence is brilliant, I hope that it will stay
This moment is perfect, please don't go away, I need you now
And I'll hold on to it, don't you let it pass you by

内容概要:本文针对国内加密货币市场预测研究较少的现状,采用BP神经网络构建了CCi30指数预测模型。研究选取2018年3月1日至2019年3月26日共391天的数据作为样本,通过“试凑法”确定最优隐结点数目,建立三层BP神经网络模型对CCi30指数收盘价进行预测。论文详细介绍了数据预处理、模型构建、训练及评估过程,包括数据归一化、特征工程、模型架构设计(如输入层、隐藏层、输出层)、模型编译与训练、模型评估(如RMSE、MAE计算)以及结果可视化。研究表明,该模型在短期内能较准确地预测指数变化趋势。此外,文章还讨论了隐层节点数的优化方法及其对预测性能的影响,并提出了若干改进建议,如引入更多技术指标、优化模型架构、尝试其他时序模型等。 适合人群:对加密货币市场预测感兴趣的研究人员、投资者及具备一定编程基础的数据分析师。 使用场景及目标:①为加密货币市场投资者提供一种新的预测工具和方法;②帮助研究人员理解BP神经网络在时间序列预测中的应用;③为后续研究提供改进方向,如数据增强、模型优化、特征工程等。 其他说明:尽管该模型在短期内表现出良好的预测性能,但仍存在一定局限性,如样本量较小、未考虑外部因素影响等。因此,在实际应用中需谨慎对待模型预测结果,并结合其他分析工具共同决策。
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