Time Series Analysis, Forecasting, and Machine Learning in Python
Python for LSTMs, ARIMA, Deep Learning, AI, Support Vector Regression, +More Applied to Time Series Forecasting
Created by Lazy Programmer Inc, Lazy Programmer Team | 22 hours on-demand video course
Welcome to Time Series Analysis, Forecasting, and Machine Learning in Python. Time Series Analysis has become an especially important field in recent years. With inflation on the rise, many are turning to the stock market and cryptocurrencies in order to ensure their savings do not lose their value. COVID-19 has shown us how forecasting is an essential tool for driving public health decisions. Businesses are becoming increasingly efficient, forecasting inventory and operational needs ahead of time. Let me cut to the chase. This is not your average Time Series Analysis course. This course covers modern developments such as deep learning, time series classification (which can drive user insights from smartphone data, or read your thoughts from electrical activity in the brain), and more.
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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