Have you ever wondered how weather predictions, population estimates, and even the lifespan of the universe are made?
Discover the power of time series forecasting with state-of-the-art ML and DL models.
The course begins with the fundamentals of time series analysis, including its characteristics, applications in real-world scenarios, and practical examples. Then progress to exploring data analysis and visualization techniques for time series data, ranging from basic to advanced levels, using powerful libraries such as NumPy, Pandas, and Matplotlib. Python will be utilized to assess various aspects of your time series data, such as seasonality, trend, noise, autocorrelation, mean over time, correlation, and stationarity.
Additionally, you will learn how to pre-process time series data for utilization in applied machine learning and recurrent neural network models, which will enable you to train, test, and assess your forecasted results. Finally, you will acquire an understanding of the applied ML models, including their performance evaluations and comparisons.
In the RNNs module, you will be building GRU, LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM models.
By the end of this course, you will be able to understand time series forecasting and its parameters, evaluate the ML models, and evaluate the model and implement RNN models for time series forecasting.
All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/A-Practical-Approach-to-Timeseries-F…
Learn data analysis techniques and handle time series forecasting
Implement data visualization techniques using Matplotlib
Evaluate applied machine learning in time series forecasting
Look at auto regression, ARIMA, Auto ARIMA, SARIMA, and SARIMAX
Learn to model LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM models
Implement ML and RNN models with three state-of-the-art projects
Every module has engaging content; a completely practical approach is used along with brief theoretical concepts. At the end of every module, there will be a quiz, followed by its solution in the next video.
This course is designed for both beginners with some programming experience and even those who know nothing about data analysis, ML, and RNNs.
The course is suitable for individuals who want to advance their skills in ML and DL, master the relation of data science with time series analysis, implement time series parameters and evaluate their impact on it and implement ML algorithms for time series forecasting.