Master Time Series Forecasting with Python : 2025
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In this engaging and hands-on course, you will master time series forecasting using Python, focusing on real-world applications. You’ll begin by understanding the core concepts of time series data, including trend, seasonality, noise, and stationarity. Learn why stationarity is critical for accurate modeling and how to transform non-stationary data using differencing, log transformations, and seasonal adjustments.
The course dives into essential forecasting techniques such as ARIMA, SARIMA, and SARIMAX, along with the mathematical intuition behind these models. You’ll gain a deep understanding of autocorrelation, partial autocorrelation, and how to interpret model parameters to optimize forecasting accuracy and prediction power.
Through practical exercises, you’ll learn how to preprocess and visualize time series data, handle missing values, and apply transformations. You will also gain hands-on experience with model selection, diagnostics, and evaluation metrics like MAE, RMSE, and AIC, helping you understand the strengths and limitations of different models.
The course covers rolling and recursive forecast approach, preparing you to predict unknown future data effectively. The significance of model evaluation will be highlighted throughout, ensuring your forecasting models are reliable. By the end of this course, you’ll be equipped to tackle real-world forecasting challenges, from sales predictions to financial forecasting. With interactive tutorials, step-by-step projects, and real-world datasets, you’ll confidently build and evaluate forecasting models in Python, gaining a solid foundation in both the theory and practice of time series analysis.
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1Introduction To ForecastingVideo lesson
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2What is Time Series ForecastingVideo lesson
Learn what is time series data and what is not with real-life examples(both continuous and discrete)
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3Resources and ReferencesText lesson
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4EnvironmentText lesson
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5Create your first time series data Structure in PythonVideo lesson
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6Assignment solution and Insights :Electricity Consumption Time SeriesVideo lesson
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7Know your Time Series: Components and DecompositionVideo lesson
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8Basic Steps in Time Series ForecastingVideo lesson
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9Time Series Forecasting Vs Regression TasksVideo lesson
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10Statistical Properties of Time Series - 1Video lesson
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11Autocovariance and AutocorrelationVideo lesson
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12White NoiseVideo lesson
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13ACF Plot and PACF PlotVideo lesson
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14StationarityVideo lesson
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15Some simple Time Series Models - MA, AR , RWVideo lesson
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16MA Process Explained with Real Life ExampleVideo lesson
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17Stationarity of Moving Average - MA (1)Video lesson
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18AR Process With ExampleVideo lesson
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19AR Process Stationarity AR(1)Video lesson
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20Random Walk , Drift and PropertiesVideo lesson
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21ADF Test for Stationarity - Intution and InterpretationVideo lesson
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22Exploring Stationarity and ACF of Simulated Random WalkVideo lesson
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23Is Google Stock Price a random walkVideo lesson
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24Forecasting A Random WalkVideo lesson
