Introduction to Time Series with Python [2025]

Learn to read excel files with pandas by using read_excel and specifying sheet_name, with default first sheet; inspect with ExcelFile and read multiple sheets into a dictionary of dataframes.

Identify missing values, their patterns, and their effects on analysis. Apply strategies like deletion, mean or median imputation, regression and multiple imputation, and expectation maximization or k-nearest neighbor methods.

Learn to handle missing values in time series with pandas, using read_csv in Colab, and inspect with isna, sum, and info on the CO2 dataset.

Apply forward fill and backward fill imputation to CO2 time series data in Python, and visualize original versus imputed series.

Implement rmse scores and plot graphs to compare imputation methods such as forward fill, backward fill, and mean across CO2 and clickstream datasets.

Explore signal processing as a subdomain of electrical engineering, analyze time series with statsmodels, and apply moving averages to hourly to annual observations.

Explore how window functions reduce edge artifacts and spectral leakage in time series analysis by tapering data with examples like rectangular, Hann, Hamming, and Blackman windows.

Implement window functions by computing the rolling mean on sales data with various window types, such as boxcar and Blackman, and plot the results.

Explore sdl decomposition to split a time series into trend, seasonal, and residual components using the seasonal_decompose method in Python.

Compute autocorrelation with numpy, compare with pandas autocorrelation plot, read the dataset, and display results using plt.plot and plt.show.

Explore autoregression models in time series, predicting the current value from past observations using order p, guided by autocorrelation and partial autocorrelation, with estimation and forecasting.

Implement autoregressive models on sunspot data by splitting into train and test sets, fitting an AR model, and evaluating with MAE, MSE, and RMSE, then plotting predictions.

Explore how Fourier analysis decomposes time-domain signals into frequency components using the Fourier transform and discrete Fourier transform, with applications in signal processing, time series analysis, and communications.

Explore Fourier transform implementation for time series with Python, computing power spectra from sunspot data and visualizing the results with plots.

Explore how time series anomaly detection identifies unusual data points and patterns using statistical thresholds, decomposition, autoencoders, and proximity methods across cyber security, finance, health care, and environmental data.

Explore the k-nearest neighbor time-series classifier with time cropping and tsfresh feature extraction, including imputing missing values, feature selection, and cross-validated performance.

Learn how to implement Silverkite for COVID time series forecasting in Python, load data with pandas, configure forecaster, evaluate with backtests, and generate 90-day forecasts with uncertainty.

Exclude outliers by using pandas boolean indexing to drop data between July 29, 2016 and September 01, 2016, then build a profit model with yearly seasonality and forecast 365 periods.

Explore how z-score standardizes data and detects outliers from the mean in units of standard deviation, using thresholds like ±3, with notes on its parametric assumptions and non-normal data alternatives.