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-Forecasting-using-Python