Data Science - Time Series Forecasting with Facebook Prophet in Python - Time Series Basics Section Introduction

The video tutorial introduces the basics of time series, defining it as a series of numerical measurements recorded over time. It highlights the flexibility of the Prophet model compared to traditional methods like ARIMA and ETS, especially in handling missing values. The tutorial covers key tasks in time series analysis, such as fitting and forecasting, and explains the concept of forecast horizon. It also discusses traditional methods, ARIMA and ETS, and their limitations. Finally, it outlines how to evaluate forecasts using metrics, baselines, and walk forward validation.

What are the limitations of ARIMA and ETS methods in time series modeling?

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What is the naive forecast and why is it significant?

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Explain the concept of walk forward validation in time series analysis.

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