In this post I show you how to predict stock prices using a forecasting model publicly available from Facebook Data Science team: The Prophet
Traditionally most machine learning (ML) models use as input features some observations (samples / examples) but there is no time dimension in the data.
Time-series forecasting models are the models that are capable to predict future values based on previously observed values. Time-series forecasting is widely used for non-stationary data. *Non-stationary data *are called the data whose statistical properties e.g. the mean and standard deviation are not constant over time but instead, these metrics vary over time.
These non-stationary input data (used as input to these models) are usually called time-series. *Some examples of time-series include the temperature values over time, stock price over time, price of a house over time etc. So, the input is a *signal (time-series) that is defined by observations taken sequentially in time.
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
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.
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