Can Prophet excel at analysing shorter time periods? However, trend, seasonality and changepoints can often be more defined across a longer time series, as longer-term characteristics of the series become more apparent. For this example, Prophet is used to conduct forecasts across two time series.
Prophet is a time series library developed by Facebook. Prophet is particularly adept at modelling a time series with significant seasonal trends, as well as those with various “changepoints” present, i.e. structural breaks in the time series.
However, trend, seasonality and changepoints can often be more defined across a longer time series, as longer-term characteristics of the series become more apparent.
For this example, Prophet is used to conduct forecasts across two time series.
While it is debatable as to what specifically consists of a “short” and “long” term time series, it will be assumed for this purpose that the weekly hotel cancellations is a short-term time series, given that there is two years of data — which is likely long enough to extrapolate long-term trend and seasonal factors.
This example is elaborated on further in a previous article titled, “Time Series Analysis with Prophet: Air Passenger Data”. The full details of the analysis as well as the relevant link to the GitHub repository containing the dataset and Jupyter Notebook can be found there.
A summary of the results are included here for illustration.
Prophet was able to outperform an ARIMA model in forecasting air passenger numbers.
However, air passenger data typically follows a predictable seasonal pattern — with passenger numbers rising throughout the summer and then descending through the winter months. Moreover, the data under analysis was over the course of a decade, which allows Prophet to factor in long-term trends and seasonality shifts into the analysis.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Statistics for Data Science and Machine Learning Engineer. I’ll try to teach you just enough to be dangerous, and pique your interest just enough that you’ll go off and learn more.
In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.