Discussing the article: "Data Science and ML (Part 42): Forex Time series Forecasting using ARIMA in Python, Everything you need to Know"
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Check out the new article: Data Science and ML (Part 42): Forex Time series Forecasting using ARIMA in Python, Everything you need to Know.
ARIMA, short for Auto Regressive Integrated Moving Average, is a powerful traditional time series forecasting model. With the ability to detect spikes and fluctuations in a time series data, this model can make accurate predictions on the next values. In this article, we are going to understand what is it, how it operates, what you can do with it when it comes to predicting the next prices in the market with high accuracy and much more.
Time series forecasting is the process of using past data to predict future values in a sequence of data points. This sequence is typically ordered by time, hence the name time series.
While we can have as many feature variables as we want in our data, any data for time series analysis or forecasting must have these two variables.
This is an independent variable, representing the specific points in time when the data points were observed.
This is the value you're trying to predict based on past observations and potentially other factors. (e.g., Daily closing stock price, hourly temperature, website traffic per minute).
Author: Omega J Msigwa