Time series prediction implement on Python

語言

英文演講

分類

資料分析

Python 難易度

進階

摘要

Time series prediction has become one of the most popular field for applying in the real world. Because there are various models to forecasting the future data, how to choose a suitable model has become a significant issue for every companies who want to join the data driven trend. In this talk, we are going to share our experience and result of the implementation of time series forecasting models. The topic will include the following points:
1. How to choose a suitable model for variety datasets,
2. Why did we choose the current models (ARIMA+SVR, SdA),
3. How to implement the models on python,
4. What problems did we face when we are implementing the model.

說明

We are going to introduce two models of time series forecasting: ARIMA+SVR and Stacked denoising autoencoders (Deep learning). We will have two to three speakers to talk about the topic.

古宣佑 Hsuanyu

目前於Soocii任職後端工程師，對機器學習演算法有興趣，工作之餘，會拿工作上面臨的問題，當作練習的題目。
之前曾經接觸過時間序列預測的模型開發，在學時期則是專注於自然語言處理(Sentiment Analysis)的相關研究。

Trudie

目前就讀於台灣大學資訊管理碩士，研究推薦系統與社群網絡，於物聯網分析公司擔任資料分析師，興趣在於開發資料分析應用。