Time Series Forecasting with Python
Forecast what comes next with Python.
About this course
Forecast what comes next with Python. Across three days you will explore time series in pandas and NumPy, decompose error, trend and seasonality with statsmodels, test for stationarity, build ARIMA, SARIMA and SARIMAX models, train recurrent neural networks in TensorFlow, model seasonality quickly with Prophet and validate predictions against test data.
Curriculum
- Introduction to time series forecasting and predictive analytics
- Exploring time series data with pandas and NumPy
- Importing and preparing datasets (import pandas as pd, import numpy as np)
- Visualizing trends, seasonal patterns, and moving averages
- Overview of forecasting involves and common real-world use cases
- Using statsmodels for statistical time series analysis
- Decomposing time series with error-trend-seasonality models
- Identifying stationary vs. non-stationary datasets
- Detecting lag structures with ACF and PACF plots
- Understanding model uses for autoregressive and MA processes
- Forecasting with ARIMA and SARIMA models
- Developing SARIMAX models to include exogenous variables
- Working with multiple variables and multiple models
- Evaluating model performance with RMSE and forecast accuracy
- Forecasting time series data and making data-driven decisions
- Introduction to deep learning for forecasting
- Building and training Recurrent Neural Networks using TensorFlow
- Using neural networks for model training and prediction
- Tuning hyperparameters and tracking model results
- Predicting the future based on historical data
- Overview of the Prophet library and its applications
- Modeling trends and seasonality with minimal tuning
- Building scalable forecasting models using Prophet
- Comparing Prophet to classical methods and machine learning models
- Forecast future values based on real datasets
- Applying random forest and other machine learning models to time series
- Performing exploratory data analysis before model fitting
- Using test data to validate predictions
- Making accurate forecasts with real-world datasets like stock price trends
- Analyzing results from statistical models, predictive tools, and neural architectures
- Using linear combination techniques in advanced models
Who it's for
Data analysts and Python users who forecast demand, sales, traffic or other values over time.
Before you start
- Introduction to Python
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