Introduction To Time Series
For data scientists and ML engineers looking for advanced techniques from the ML field, this Python guide is ideal.
Many ML resources focus on classification and regression, but time series analysis is often overlooked. It is crucial in the world of business, as time is a fundamental dimension. Therefore, time series data is incredibly valuable and prevalent.
An organization can gain significant value from the analysis of time series data. Time series analysis was first introduced during the 1920s and 1930s. Currently, there is a lot of data and more attention is being paid to machine learning, which has changed the landscape of time series analysis. Aiming to go beyond traditional methods such as ARIMA, this article introduces the newest machine learning techniques in time series analysis. After exploring fundamental concepts, we will gradually move on to more complex topics. — Understanding the data generation process (DGP) — Defining a time series — Potential forecasts — Terminology and notation.
As per new medium rules, I need to specify that no part of this article is AI generated. Entire article was written by a living creature.
What Is Time Series?
Typically, time series are arranged in chronological order, with periodic data points.
Sequential observations over a period of time are called a time series. Focus is placed on the element of time. Time series can be obtained by recording the same observation repeatedly at different intervals of time. By tracking the monthly amount of chocolate bars you possess, you could come up with a time series showing how much chocolate you consume on a monthly basis. You could also record your weight at the beginning of every month to develop another time series of weight measurements. This article will provide a scientific analysis of the correlation between these two-time series. As an additional example of a time series, you might record the daily rainfall or snowfall in your city or the hourly readings of your heart rate from a smartwatch.