Time Series Analysis in Python - Data Analysis & Forecasting

Learn to work with time series data in pandas by importing pandas as pd, setting the date index, and applying three- and twelve-month rolling averages and a leg plot.

Create and index one- and two-dimensional NumPy arrays in Python. Practice zero-based indexing and bracket notation to access specific elements, including 2D elements by row and column.

Explore NumPy basics: array slicing and iterating for one- and two-dimensional arrays, using indexing and for loops with in-loop operations, including matrix multiplication.

Explore NumPy, a Python library for arrays and fast mathematical operations, including linear algebra, Fourier transforms, and matrices. Learn about multi dimensional arrays, dimensions, and basic array creation and indexing.

Master practical techniques for handling missing values in time series data, including dropna, forward fill, backward fill, and interpolation, alongside detecting and addressing outliers with visual inspection and z-score methods.

Master handling time zones and date time formats in time series data with pandas by localizing and converting time zones, parsing strings with to_datetime, and formatting timestamps for presentation.

Learn resampling and interpolation techniques for time series in Python, including downsampling, upsampling, and linear interpolation with Pandas.

Plot time series data with line plots, scatter plots, histograms, and box plots using matplotlib and pandas to visualize time stamps and values, revealing patterns and behavior.

Explore exploratory data analysis techniques for time series data, calculating summary statistics with pandas, visualizing trends with line plots, decomposing seasonal patterns, and assessing autocorrelation.

Identify trends in time series data using rolling average and visualize them with line plots to reveal long-term patterns.

Define stationarity and its importance in time series analysis, noting constant mean, variance, and autocovariance. Compare stationary and non-stationary examples and introduce Python tests for forecasting and ARIMA modeling.

Apply the augmented Dickey-Fuller test to determine time series stationarity in Python using statsmodels, interpret p-values against a 0.05 significance level, and understand unit roots, test statistics, and critical values.

Forecast electricity consumption with time series analysis in Python, using an auto regressive model, pandas, and a 12-month rolling window for yearly averages and seasonality visualization.

analyze the time series in python, reveal seasonality with added lines and seasonal decomposition, test stationarity, and evaluate autoregressive models via AIC across lags to forecast 24 months with visualization.

Explore arima models for time series forecasting by combining autoregressive, differencing, and moving average components; implement in Python with statsmodels to fit, forecast, and interpret coefficients, standard errors, and tests.

Explore exponential smoothing models, including simple, linear, and Holt-Winters, to capture trend and seasonality in time series with Python.