Mastering Time Series Forecasting with Python

Explore line charts for time series by plotting sales over time with Seaborn and Pandas, revealing trend and seasonality and how to adjust size and index for clarity.

visualize time series data with hue in a seaborn line chart to separate series by year and quarter, extract year and quarter from date columns, and apply color maps to reveal patterns.

Learn to create area charts from time series data in Python by setting the index and figure size, and explore stacked area plots, with upcoming preprocessing and visuals coverage.

Learn to preprocess quarterly sales data, group by year and quarter, reshape into a 9x4 matrix, and create stacked bar and area charts to visualize time series trends.

Explore exploratory data analysis with graphical techniques in Python, using distribution plots, KDE, histograms, box plots, and scatterplots to understand X and Y distributions, skew, covariance, and outliers.

split the time series data into training and testing sets to evaluate a simple linear regression, using time as the independent variable and sales as the dependent variable.

Learn to integrate time into linear regression to model trends in time series data using Python, train the regression model, and evaluate predictions with training and test data.

Create seasonal features by one-hot encoding month names, generate and align dummy variables, and concatenate them with train and test data for consistent time series forecasting.

Compute the average seasonal deviation for each month by grouping deviations by month name and averaging across years to form the seasonal corrector factor.

Calculate the margin of error for the train data using standard error and a 95% confidence interval, where the standard error equals the error and represents the variation of errors.

Visualize forecasted data alongside true values, compute confidence intervals and margins of error, and compare multiplicative and additive models to assess fit over time.

Analyze ACF and PACF plots to identify lag order for autoregressive models, detect seasonality, and decide when to remove trend for stationarity.

Build an auto regressive model for time series forecasting by selecting the lag order p, preprocessing data, and training the model with a single column and month index.

Apply a naive forecasting model in python by preprocessing airline passenger data in colab, converting month to datetime, visualizing seasonal patterns, and performing a train–test split.

Learn the simple moving average as a smoothing method for time series, forecasting future values from past data using an order set by the autocorrelation function.

Build a simple moving average model of order q in Python, using a rolling window and mean to forecast future values from the data.

Mastering time series forecasting with Python explains weighted moving average, assigning higher weights to recent values (T-1, T-2, T-3) to improve forecasts and build the model.

Explore exponential smoothing and exponential moving average, which weight past values with exponentially decreasing importance. Use alpha, the smoothing constant between 0 and 1, to forecast and analyze error.

Build an exponential moving average model in Python to forecast future values from train and test data in pandas, tuning alpha via grid search and comparing to simple moving average.

Evaluate your ARMA model by computing the AIC information criterion and mean square error to assess goodness of fit on train data and forecasts, then visualize the model output.

Visualize ARIMA forecasts by plotting forecasted values with their confidence intervals against the time index. Adjust the lower and upper limits, alpha, and colors to clearly show forecast uncertainty.