Mastering Time Series Analysis and Forecasting with Python

Name: Mastering Time Series Analysis and Forecasting with Python
Rating: 2.8 (6 reviews)

Master time series forecasting using Python with ARIMA, SARIMA, Facebook Prophet & hands-on projects.

Created byAkhil Vydyula

Last updated 7/2024

English

English [Auto],

What you'll learn

Understand the fundamentals of time series analysis, including trends, seasonality, and noise.
Implement various time series forecasting methods such as ARIMA, SARIMA, and Prophet using Python.
Evaluate and tune time series models to improve accuracy and performance.
Apply time series analysis techniques to real-world datasets and interpret the results for actionable insights.
Students and researchers interested in applying time series techniques to their projects.
Data analysts and scientists looking to enhance their time series analysis skills.
Professionals working in fields like finance, economics, and operations who deal with time-series data.
Anyone curious about understanding and predicting patterns in time-dependent data.

Course content

5 sections • 15 lectures • 2h 45m total length

Introduction to Time Series Data10:01
Learn the basics of time series data and forecasting, including interpreting plots, mean and standard deviation, and the roles of trend, seasonality, and stationarity in sales and pricing.
Understanding Time Series Components12:38
Explore time series components such as trend, seasonality, irregularity, and cyclicity; test stationarity with rolling statistics and ADF test, then apply differencing and transformations to prepare data for ARIMA models.
Stationarity and Its Importance8:06
Master time series analysis with moving averages, autocorrelation and lag concepts, and apply ARIMA and SARIMA models for forecasting seasonality and trend.

ARIMA Model Fundamentals9:32
Explore Arima model fundamentals, including autoregression, moving average, and integration, with p, d, q parameters, hyperparameter tuning via grid and random searches, and time series decomposition in Python.
Building and Evaluating ARIMA Models12:51
Learn to convert non-stationary time series to stationary with differencing and transformations, assess with ADF p-values, and forecast using ARIMA, Prophet, and feature engineering for real-world data like Bitcoin.
Seasonal Time Series and Decomposition11:42
Explore the statistics foundations for seasonal time series and forecasting, including central tendencies, distributions, hypothesis testing, and model validation, with practical guidance on Gaussian assumptions, sampling, and outlier detection.

Probability Distributions in Time Series14:12
Explore the central limit theorem, showing how sample means form a gaussian distribution and how to simulate, plot, and analyze them in time series, including skewness handling.
Descriptive Statistics and Exploratory Data Analysis16:54
Master descriptive statistics for time series forecasting by exploring distributions, mean and median, variance and standard deviation, z score, percentiles, p-values, hypothesis testing, t-tests, and confidence intervals.
Hypothesis Testing and Confidence Intervals11:52
Explore hypothesis testing and confidence intervals with variance, p-values, independence assumptions, and normal distributions. Analyze Pearson correlation with heatmaps, iris data, and ARIMA forecasting with hyperparameter tuning.

Forecasting with ARIMA Models15:35
Learn how ARIMA models generate forecasts with confidence intervals, upper and lower bounds, and mean predictions, using training and forecasting data to reveal trends and seasonality.
Model Selection and Evaluation9:24
Apply basic statistics to real-world data using histograms, box plots, and IQR to analyze diagnosis and radius mean, detect outliers, and interpret Gaussian distributions.
Practical Forecasting and Model Improvement7:15
Apply descriptive statistics, distributions, and correlation analyses in Python to improve time series forecasting models, using pandas describe, CDFs, histograms, and univariate to multivariate methods.

Data Visualization for Time Series8:17
Learn to visualize and interpret relationships in data with joint plots, q-q plots, distribution plots, and pair plots, examining radius mean and area mean, skewness, and monotonic trends.
Time Series in Python: Practical Implementation7:45
Learn practical time series feature analysis in Python using scatter, density, and pair plots to explore correlations among F1, F2, and F3, identify outliers, and interpret heatmaps and color maps.
Real-world Case Studies and Applications9:29
Explore covariance and Pearson correlation, central tendencies, and imputation of missing values using mean, mode, or median, plus distributions, histograms, z-scores, and basic hypothesis testing.

Requirements

Basic knowledge of Python programming. Familiarity with libraries such as pandas and matplotlib is beneficial.
A computer with internet access to follow along with coding exercises and access datasets.
Basic understanding of statistical concepts such as mean, variance, and correlation.
Willingness to learn and apply analytical thinking to solve time series problems.
A curious mind and willingness to learn!
Familiarity with statistical concepts (mean, median, standard deviation).
Basic understanding of Python programming.
Curiosity and a willingness to learn analytical problem-solving with time series data.

Description

Are you ready to master the powerful skill of time series forecasting and make data-driven decisions using Python?

In this comprehensive course, Mastering Time Series Analysis and Forecasting with Python, you will learn how to analyze temporal data and predict future outcomes using popular techniques such as ARIMA, SARIMA, and Facebook Prophet.

Whether you're new to time series or an experienced data scientist looking to enhance your forecasting skills, this course is designed for all levels. With a focus on practical implementation and real-world applications, you'll gain hands-on experience using datasets from finance, marketing, retail, and other industries.

This course offers step-by-step guidance on essential statistical modeling, data transformation, and visualization techniques. You will learn to preprocess time-based data, explore and detect patterns, and build reliable models for forecasting future events.

By completing the course, you will be able to apply powerful tools from Python's data science stack including Pandas, Matplotlib, Statsmodels, and Prophet. You’ll also develop the ability to evaluate models using error metrics and improve model accuracy through parameter tuning.

Who Should Enroll:

Aspiring data scientists and analysts
Business intelligence professionals
Students and researchers analyzing time-based data
Professionals in finance, operations, and marketing who use forecasting for decision-making

Gain valuable skills to work with time-based data effectively and confidently. Enroll now and start your journey to mastering time series forecasting with Python.

Who this course is for:

Aspiring data scientists and analysts looking to specialize in time series analysis and forecasting.
Professionals in finance, marketing, operations, and other fields where time series data is commonly used for decision-making.
Students and researchers in academia who need to analyze time series data for their studies or projects.
Anyone interested in gaining practical skills in time series analysis to enhance their data science toolkit.
Enthusiasts interested in learning how to forecast trends and patterns in time-dependent data.

Mastering Time Series Analysis and Forecasting with Python

What you'll learn

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Course content

Foundations of Time Series Analysis3 lectures • 31min

Time Series Modeling with ARIMA3 lectures • 34min

Statistical Concepts for Time Series3 lectures • 43min

Forecasting with Time Series Models3 lectures • 32min

Advanced Time Series Topics and Applications3 lectures • 26min

Requirements

Description

Who this course is for: