The observed data is forecasted using Bayesian Neural Networks
The exponential smoothing and moving average are the two basic and important techniques used for time series forecasting.
EvalMl is a python library for automated machine learning that helps us in building, optimizing, and evaluating machine learning pipelines.
The prophet is a toolkit or library for time series analysis that is available to us as an open-source. Utilizing this toolkit we can perform time series analysis and forecasting very easily and fast. This toolkit has various features that can make our time series analysis procedure accurate and efficient.
In time series modelling, feature engineering works in a different way because it is sequential data and it gets formed using the changes in any values according to the time
The Hodrick–Prescott filter or Hodrick–Prescott decomposition is a mathematical tool that is used in time series analysis and modelling. This filter is mainly useful in removing the cyclic component from time-series data.
Kats stands for Kits to Analyze Time Series, which was developed by the researchers at Facebook, now Meta. One of the most important things about Kats is that it is very easy to use. Also, it is a very light weighted library of generic time series analysis in a very generalized nature.
In time series analysis and forecasting, autocorrelation and partial autocorrelation are frequently employed to analyze the data.
Arauto is an open-source project for time series analysis using which we can perform various analyses on our time series data. Also, we can use various time series models from the ARIMA family using it.
Measuring the performance of any machine learning model is very important, not only from the technical point of view but also from the business perspective.
When a time series data gets collected, there is other additional information that also gets collected along with it. This important information embedded in the time-series data must be described as its characteristics.
A major criticism against Prophet is that its underlying assumptions are simplistic and weak.