Time series analysis is one of the most important topics in data science. Time series data is a sequence of data points collected and indexed based on an interval of time, and when we analyze such data to find patterns over a time interval, it is known as time series analysis. If you want to know the best approaches for time series analysis that you need to know, this article is for you. In this article, I will introduce you to some of the best approaches for solving time series analysis problems.
Best Approaches for Time Series Analysis
ARIMA is a popular Time Series forecasting algorithm, stands for Autoregressive Integrated Moving Average. This algorithm predicts a value according to the linear combination of historical data of a time series dataset. Using ARIMA is a powerful and flexible way for Time Series Analysis. You can learn more about using ARIMA for Time Series Analysis from here.
SARIMA is another popular Time Series forecasting algorithm, stands for Seasonal Autoregressive Integrated Moving Average or Seasonal ARIMA. If your time series data have seasonal patterns (one of the important components for your time series modelling), then the SARIMA algorithm is preferred over the ARIMA algorithm. You can learn more about using SARIMA for Time Series Analysis from here.
LSTM is a neural network architecture, stands for Long Short Term Memory network. It is a type of recurrent neural network preferred when you need your model to remember the data for a long period. LSTM is used in Time Series forecasting and various other problems based on regression analysis. You can learn the implementation of LSTM from here.
Facebook Prophet Model
The Facebook Prophet model is used in time series forecasting based on a dataset with non-linear trends with yearly, weekly, and even daily seasonality and holiday effects. It is an automatic time series model created by Facebook developers. If you use R or Python for Data Science, you can use this model. You can learn more about the Facebook prophet model for time series analysis from here.
AutoTS is an automatic machine learning library in Python, developed for automatic time series forecasting. Mostly, I prefer this library for forecasting stock prices and cryptocurrencies. You can find its implementation using Python from here.
Time Series data is a sequence of data points collected and indexed according to an interval of time, and when we analyze such data to find patterns over an interval of time, it is known as Time Series Analysis. I hope you liked this article on the best approaches for time series analysis. Feel free to ask valuable questions in the comments section below.