Time series prediction implement on Python


English talk


Data Analysis

Python Level



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.


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

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。