Time Series Analysis, Forecasting, and Machine Learning
Time Series Analysis, Forecasting, and Machine Learning
Python for LSTMs, ARIMA, Deep Learning, AI, Support Vector Regression, +More Applied to Time Series Forecasting
Time series analysis is an important field in machine learning that has become increasingly popular in recent years. It is used to analyze data that varies over time and is used in many applications such as finance, economics, and weather forecasting.
There are many courses available online that teach time series analysis and forecasting using machine learning. One such course is offered on Udemy called “Time Series Analysis, Forecasting, and Machine Learning in Python” 1. This course covers topics such as time series preprocessing, forecasting models, and machine learning algorithms for time series analysis.
Another article I found on arXiv.org provides a comprehensive survey of time series modeling for predictive purposes 2. The article covers topics such as time series preprocessing, modeling techniques, and evaluation metrics.
What you'll learn
- ETS and Exponential Smoothing Models
- Holt's Linear Trend Model and Holt-Winters
- Autoregressive and Moving Average Models (ARIMA)
- Seasonal ARIMA (SARIMA), and SARIMAX
- Auto ARIMA
- The statsmodels Python library
- The pmdarima Python library
- Machine learning for time series forecasting
- Deep learning (ANNs, CNNs, RNNs, and LSTMs) for time series forecasting
- Tensorflow 2 for predicting stock prices and returns
- Vector autoregression (VAR) and vector moving average (VMA) models (VARMA)
- AWS Forecast (Amazon's time series forecasting service)
- FB Prophet (Facebook's time series library)
- Modeling and forecasting financial time series
- GARCH (volatility modeling)
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