Foundations of Time Series Analysis
Introduction to Time Series and Google Colab
- Overview of time series data
- Setting up Google Colab for time
series work
- Loading datasets (CSV, APIs, Drive,
etc.)
- Introduction to Pandas DateTime
indexing
Time Series Data Preprocessing
- Converting columns to datetime
objects
- Setting time indices and sorting
- Handling missing timestamps and
duplicate values
- Frequency conversion (resampling,
upsampling, downsampling)
Exploratory Time Series Visualization
- Line plots and time-indexed
visualizations
- Plotting rolling means and standard
deviation
- Visualizing seasonality and trends
- Introduction to autocorrelation
(ACF) and partial autocorrelation (PACF)
Decomposition, Smoothing, and Forecasting Models
Time Series Decomposition
- Trend, seasonal, and residual
components
- Additive vs multiplicative
decomposition
- Using statsmodels.tsa.seasonal_decompose
- Visual interpretation of components
Smoothing Techniques and Stationarity
- Simple moving average (SMA)
- Exponential moving average (EMA)
- Differencing to remove
trend/seasonality
- Augmented Dickey-Fuller (ADF) test
for stationarity
Forecasting with ARIMA
- Understanding AR, MA, and ARIMA
concepts
- Parameter selection (p, d, q)
- Auto ARIMA with pmdarima
- Building and evaluating ARIMA
models
- Residual analysis and diagnostics
Advanced Forecasting and Real-World Projects
Forecasting with Facebook Prophet
- Introduction to Prophet for
business forecasting
- Preparing data for Prophet (ds, y
format)
- Adding holidays and seasonality
- Forecast visualization and
components
- Cross-validation in Prophet
Evaluating Forecast Accuracy
- Train-test split in time series
- Performance metrics: MAE, MSE,
RMSE, MAPE
- Visualizing forecast vs actual
- Tuning and improving model accuracy
Case Study and Dashboarding
- Build end-to-end time series
workflow in Colab
- Combine charts, code, and markdown
for presentation
- Tips for sharing and exporting
notebooks
Final Exercises and Wrap-up
- Present forecasts with visual
insights
- Peer review and instructor feedback
- Q&A and key takeaways
Final
Assessment:
- Quiz on concepts and syntax
- Code challenge: Create a forecast
and evaluate performance
