摘要: Fundamental concepts around time series analysis and time series forecasting, including everything from classical approaches to modern machine learning models with examples in Python


What would you do if you knew what the future would look like? That answer might be “buying bitcoin in 2017” for some people. Or for retailers, that answer could be “stocking up on toilet paper or silicon chips in 2019”. Whatever your answer is, we can agree that having information about the future would be advantageous.

Whatever your answer is, we can agree that having information about the future would be advantageous.

Although we have been asking fortune tellers what the future holds for us for a long time, predicting the future is still a challenge. Even with our technology today, the predictions about the weather an hour from now can be entirely off sometimes. However, for some use cases, we can model the behavior of a time series well enough to make quite accurate predictions about the future.

Time series analysis and forecasting are two broad topics that can be overwhelming. Thus, this article will introduce you to the essential concepts of time series analysis and forecasting. To showcase some concepts, we will use code examples in Python and methods from the library statsmodels [10].

轉貼自: /medium.com

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