Deep learning, time series classification (which may drive user insights from smartphone data or read your thoughts from electrical activity in the brain), and other recent technologies are covered in this course. This course will cover applications such as time series forecasting of sales data, predicting stock prices and stock returns, and classification of smartphone data to predict user behavior.

In this course you will learn ETS and Exponential Smoothing Models, Holt's Linear Trend Model and Holt-Winters, Autoregressive and Moving Average Models (ARIMA), Seasonal ARIMA (SARIMA), and SARIMAX, Auto ARIMA, The statsmodels Python library, The pmdarima Python library, Machine learning for time series forecasting, Deep learning (ANNs, CNNs, RNNs, and LSTMs) for time series forecasting, Tensorflow 2 for predicting stock prices and returns, Vector autoregression (VAR) and vector moving average (VMA) models (VARMA), AWS Forecast (Amazon's time series forecasting service), FB Prophet (Facebook's time series library), Modeling and forecasting financial time series, GARCH (volatility modeling). If you are looking to learn and understand more of the above, then this course is suitable for you. So what are you waiting for? Signup now to get lifetime access, a certificate of completion you can show off on your LinkedIn profile, and the skills to use the latest time series analysis techniques that you cannot learn anywhere else.

They will cover applications such as:

• Time-series forecasting of sales data
• Time-series forecasting of stock prices and stock returns
• Time series classification of smartphone data to predict user behavior.

The VIP version of the course will cover even more exciting topics, such as:

• AWS Forecast (Amazon's state-of-the-art low-code forecasting API)
• GARCH (financial volatility modeling)
• FB Prophet (Facebook's time series library)

Who is this course for?

• Anyone who loves or wants to learn about time series analysis
• Students and professionals who want to advance their careers in finance, time series analysis, or data science

Requirements:

• Decent Python coding skills.
• Numpy, Matplotlib, Pandas, and Scipy (They teach this for free!)
• Matrix arithmetic
• Probability

Udemy rating: 4.7/5

Enroll here: https://www.udemy.com/course/time-series-analysis/

As the global economy continues to deteriorate, corporate forecasting has become increasingly more critical. This specialty focuses on Excel Skills for Business Forecasting. This specialization includes three courses: Time Series Models, Regression Models, and Judgmental Forecasting.

Topics in this forecasting course include:

• Excel Time Series Models for Business Forecasting
• Excel Regression Models for Business Forecasting
• Judgmental Business Forecasting in Excel

In the first Time Series Models course, you will study how your company may use time-series data sets to analyze the components underlying the data and then apply the applicable model to estimate your company's needs based on these components. The following course in this specialty focuses on regression models, which allows us to construct causal models using both time-series and cross-sectional data. In causal models, they may adjust the inputs to get the desired outcome, allowing us to discover new insights and tactics. In the last course, they will look at the function of judgemental forecasting when more quantitative forecasting approaches fail to satisfy their goals and they need additional business understanding. Finally, they will look at all of these forecasting approaches together, allowing you to create predictions that will be utilized as planning inputs.

In this course, they extend your business forecasting expertise from the first two courses of their Business Forecasting Specialisation on Time Series Models and Regression Models. You will explore the role of judgmental forecasting when more quantitative forecasting methods have limitations and you need to generate further business insights. You will be exploring some structured methodologies to create judgmental business forecasts using Business Indicators, Subjective Assessment Methods, and Exploratory Methods. For each of these methods, you will look at how you can use Excel to help you in achieving these judgmental forecasts and how Excel can help you visualize your forecast findings. Being judgmental forecasting methods, they will also look at the role of biases in Business Forecasting,

This course offers:

• Earn a Certificate upon completion
• 100% Online
• Flexible Schedule
• Intermediate Level
• Approximately 4 months to complete
• Subtitles: English

Coursera rating: 4.8/5

Enroll here: https://www.coursera.org/specializations/excel-skills-for-business-forecasting

Because of current technology, more and more data is becoming available every day. Successful businesses understand this. They also acknowledge that judgments made by projecting the future based on historical data may have a big influence. Thus, understanding time series analysis and forecasting will provide you with the information and abilities necessary to develop those models.

You will learn about several methods for dealing with date and time data in R. Time zones, leap years, and varied formats make date and time computations extremely difficult for programmers. You will learn about the POSIXt classes in R Base, as well as the chron package and, in particular, the lubridate package. You will learn how to view, clean, and prepare data. As an analyst, data preparation consumes a significant portion of your time. Knowing the best outlier detection, missing value imputation, and visualization algorithms can save your day.Following that, you will study statistical approaches for time series. Autocorrelation, stationarity, and unit root tests will be discussed.

Then you'll explore how various models function, how to put them up in R, and how to utilize them for forecasting and predictive analytics. ARIMA, exponential smoothing, seasonal decomposition, and basic models serving as benchmarks are among the models taught. Of course, all of this is complemented by several workouts.

Who is this course for?

• This course is for people working with time series data.
• This course is for people interested in R
• This course is for people with some beginner knowledge of both R programming and statistics.
• This course is for people working in various fields like (and not limited to): academia, marketing, business, econometrics, finance, medicine, engineering, and science.
• Generally, if you have time series data on your table and you do not know what to do with it, take this course!

Requirements:

• computer with R and RStudio ready to use.
• interest in statistics and programming.
• time to solve the exercises.
• basic knowledge of R (course R Base)
• NO advanced statistics or maths knowledge required.

Udemy rating: 4.4/5.

Enroll here: https://www.udemy.com/course/time-series-analysis-and-forecasting-in-r/

For many people, facing the future is a surprise, if not a shock. Strong predicting abilities, on the other hand, can help you avoid future shocks. Adapting faster permits you to reap the benefits of a change sooner. This training will help you improve your predicting abilities.

In this forecasting course, you will:

• Collect and analyze signals of change.
• Identify and analyze global drivers of change.
• Combine Signals and Drivers into Future Forecasts.
• Write scenarios that tell a story about your future forecast.

You'll learn how to transform groupings of signals (future forecasting cues) and drivers (global factors determining change direction) into reliable future forecasts. Forecasts are designed to assist you in discovering new possibilities and chances for yourself, your organization, or the community you wish to inspire to improve the future. Practicing forecasting abilities will help you better predict the future. As a consequence, you will be able to contemplate ideas that others would not. You will assist others in adapting to and preparing for the future. You'll also learn how to write future scenarios. Scenarios take forecasts one step further. They spark imagination and tell a story about what might happen if a forecast comes true. Scenarios help you evaluate: "Is this a future I'm ready for?" Is this the future I want?'

Leading futurists from the Institute for the Future will show you exactly how it's done. They'll share with you the forecasts and scenarios they’re most excited about right now, and walk you through the key steps they took to create them. Then, it's your turn! You'll create a forecast and a scenario on any future topic you choose. How will you benefit from taking this course? With strong forecasting skills, you'll get better at seeing the future before it happens. You'll be ready to consider possibilities that others never see coming or refuse to accept. You'll be able to help others prepare for and adapt to the future. You can decide which futures you want to make more likely, and which futures you want to prevent.

This course offers:

• Flexible deadlines
• Earn a certificate upon completion.
• 100% online
• Beginner Level
• Approximately 13 hours to complete.
• Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Coursera rating: 4.8/5

Enroll here: https://www.coursera.org/learn/forecasting-skills

Forecasting is always enticing; knowing what will happen typically leaves people in awe. Furthermore, it is critical in the corporate sector. Revenue growth and EBIT estimations are always provided by companies and are based on forecasts. It is critical that you understand why a model makes sense and the underlying assumptions that underpin it. They'll explain each model to you using words, diagrams, and metaphors, avoiding arithmetic and the Greek letters as much as possible. They will guide you through every step of the way in your journey to mastering time series and forecasting models. They will also explain all the parameters and functions that you need to use, step by step.

There is a challenge for each algorithm. That is, each approach consists of two case studies. The idea is for you to use what you've learned right away. They will provide you with a dataset and a list of steps you must follow to solve it. It's believed that this is the best method to truly embed all of the skills in you. This is one of the best online forecasting courses.

The techniques in this course are the ones it believes will be the most impactful, up-to-date, and sought-after:

• Holt-Winters
• TBATS
• SARIMAX
• TensorFlow: Structural Time Series
• Facebook Prophet
  
• Facebook Prophet + XGBoost
• Ensemble approach

Who is this course for?

• Professionals looking to learn about demand forecasting and time series

Requirements:

• Basic Statistics: Linear Regression, p-value
• Basic Python is desirable.

Udemy rating: 4.5/5

Enroll here: https://www.udemy.com/course/forecasting-python/

Sales forecasting is critical for nearly every firm because it influences sales deployment, financial planning, budgeting, operations planning, and marketing planning. Because sales projections have such a broad influence, it is vital that the forecast data be as precise as possible. In this course, Drew Boyd, a sales and marketing professor, explains a step-by-step approach for establishing and monitoring excellent sales predictions. He walks you through the steps of defining your market category, developing the necessary processes, selecting the appropriate forecasting approach, and gathering data. He also demonstrates how to apply both qualitative and quantitative forecasting approaches.

This course will show you how to generate and manage efficient sales projections step-by-step. This course will teach you how to establish your market category, create the necessary processes, choose the appropriate forecasting approach, and collect data. It also shows how to use both qualitative and quantitative forecasting approaches. This is one of the best online forecasting courses.

Topics in this forecasting course include:

• Understanding Sales Forecasting
• Preparing for Sales Forecasting
• Using quantitative forecasting
• Using qualitative forecasting

What’s included:

• Practice while you learn (1 exercise file)
• Test your knowledge with these quizzes.
• Learn on the go on your tablet and phone.

Linkedin: 4.8/5

Enroll here: https://www.linkedin.com/learning/sales-forecasting-2022?replacementOf=sales-forecasting-2017

The course, presented by the IMF Fiscal Affairs Department, provides hands-on learning to help students build foundational knowledge of the various quantitative models and techniques that can be used to forecast revenues and conduct tax policy analysis, while also exploring related issues such as the characteristics of a sound institutional framework and the key principles of tax policymaking.

Income forecasting is the practice of estimating future government revenue streams using analytical approaches. It not only assists policymakers in developing solid public policies, but it also helps them plan public spending based on predicted income. Even while governments generate income from a variety of sources, taxes are often the most significant. As a result, the emphasis of this course is on estimating and assessing tax income.

This course aims to provide students with a solid understanding of the different mathematical models and approaches required to anticipate revenues and undertake tax policy research. It will also cover related issues such as the features of a solid institutional structure and tax policy ideas.

This course offers:

• Institution: IMFx
• Subject: Economics & Finance
• Level: Intermediate
• Prerequisites: Some knowledge of taxation and/or economics is helpful. Basic Microsoft Excel skills and access to a computer with a reliable Internet connection are essential.
• Language: English
• Video Transcript: English

Edx rating: 4.6/5

Enroll here: https://www.edx.org/course/revenue-forecasting-and-analysis

Exponential smoothing refers to a collection of simple forecasting algorithms that use self-correction. Each prediction is made up of two parts. It's a weighted average of the previous forecast plus a modification that would have improved the accuracy of the previous forecast. Smoothing, like other credible forecasting systems, requires a baseline of data in order to function effectively. Weekly earnings and daily hospital admissions are two common examples. There are several forms of exponential smoothing, each matched to a different sort of baseline.

Conrad Carlberg is an experienced information professional with a deep background in statistics and analytics, applied to fields as diverse as hospital process measurement, financial planning, and telecommunications. Baby Bell and major account sales at Motorola ability to communicate, from the C-suite to IT, the substance and potential of analytic techniques. It has an innovative approach to designing analytics and a proven, pragmatic approach to carrying them out. I have extensive experience collaborating with sales, technical, and management divisions. The author of nearly twenty well-regarded books on statistical and business analysis has an academic background in numeric analysis, including a Ph.D. in statistics from the University of Colorado. He introduces simple exponential smoothing in this course, delving into the underlying theory behind it and teaching how to compose the prediction equation and optimize forecasts.

This course will teach you how to put together a prediction equation and optimize forecasts, as well as an introduction to simple exponential smoothing. This is one of the best online forecasting courses.

Topics in this forecasting course include:

• The Idea Behind Exponential Smoothing
• The Forecasting Equation
• Measuring Forecast Accuracy.
• Optimizing Forecasts

What’s included:

• Practice while you learn (2 exercise files)
• Test your knowledge (3 quizzes)
• Learn on the go (Access on tablet and phone)
• Stay up to date (Continuing Education Units)

Linkedin: 4.1/5

Enroll here: https://www.linkedin.com/learning/business-analytics-forecasting-with-exponential-smoothing

In this macroeconomics course, you will learn to forecast macroeconomic variables such as inflation, growth, and consumption, as well as to develop statistical models in economics and use them to forecast economic policy responses. You will learn through hands-on demonstrations of model-building, forecasting, and policy analysis utilizing data sets from a range of nations. EViews, a popular software for estimating and modeling forecasting models on Windows, will be used in demonstrations and applications. For the length of the course, free, temporary EViews licenses will be made accessible.

The Institute for Capacity Development's course is meant to strengthen participants' macroeconomic forecasting and modeling skills, as well as their use of current econometric tools. The underlying theory is discussed in lectures, and demonstrations illustrate how to do empirical investigations with EViews. This is one of the best online forecasting courses. Participants are expected to have a background in undergraduate statistics and basic econometrics. Access to a computer with a reliable Internet connection and a Google Chrome web browser is essential. Demonstrations and applications are conducted using EViews – a popular software for estimating and simulating forecasting models in Windows. Temporary licenses for EViews are made available for the duration of the course.

In this forecasting course, you will learn:

• Evaluation of macro-econometric models.
• Forecasting of uncertainty and forecasting for policy analysis.
• Properties of time series data and model design.

What's included:

• Institution: IMFx
• Subject: Economics & Finance
• Level: Intermediate
• Prerequisites: A background in statistics and economics at the undergraduate level is assumed.
• Language: English
• Video Transcript: English

Edx rating: 4.3/5

Enroll here: https://www.edx.org/course/macroeconometric-forecasting-2