8 Best Time Series Courses in 2026 (Classical + ML, Verified)

Last updated: October 2026. Written by Josh Hutcheson, OnlineCourseing editor. We re-opened every course below on 24 October 2026 and read its rating, enrollment, length and last-update date from the provider’s own page that day. See our review methodology.

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Time series is where data science earns its keep: demand forecasts, capacity planning, anomaly alerts and financial models all run on it. It is also taught worse than almost any other topic, stranded between statistics courses that stop at ARIMA and machine learning courses that shuffle the data as if yesterday did not predict today. We re-opened every course below on 24 October 2026, read the forecasting threads on r/datascience and r/learnmachinelearning, and ranked the options on how honestly they teach both halves.

What is time series analysis?

A time series is a set of measurements taken in order over time: daily sales, hourly electricity load, monthly unemployment, a server’s CPU every second. Time series analysis studies the structure of that sequence (its trend, its seasonal pattern, its cycles and its noise), and time series forecasting uses that structure to predict future values. The defining feature is that order matters. Each observation is related to the ones before it, which breaks the “independent rows” assumption most machine learning is built on.

A few terms you will meet in every course: stationarity (the series’ statistical behavior does not drift over time), differencing (subtracting each value from the previous one to remove a trend and make a series stationary), autocorrelation (how strongly a value relates to its own past), and seasonality (a pattern that repeats on a fixed calendar, such as weekly or yearly). Time series classification is a different task: labeling whole sequences, such as heartbeat recordings, rather than predicting the next value.

The best time series courses in 2026

1. Time Series Analysis, Forecasting, and Machine Learning — Udemy (best overall)

The rare course that covers the whole modern toolkit honestly. It starts with the methods that still win many real forecasting problems (exponential smoothing, ARIMA and its seasonal variants), then moves through machine learning approaches such as support vector machines and random forests, vector autoregression, GARCH volatility models, Facebook Prophet, AWS Forecast, and deep learning (ANNs, CNNs, RNNs and LSTMs), with implementation in Python throughout. On 24 October 2026 it had 4.6 stars from 3,156 ratings, 12,963 students and Udemy’s Bestseller and Highest Rated badges, and it was last updated in March 2026. Its listed requirements are decent Python, NumPy, Matplotlib, pandas and SciPy, matrix arithmetic and probability, and it also covers time series classification, which most forecasting courses skip. Individual Udemy course purchases come with a 30-day refund window.

Best for: working analysts and data scientists who want one course that covers everything.  Worth knowing: it is long; if you are new to pandas, start with the DataCamp track (#4) or Portilla (#5).

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2. Practical Time Series Analysis — The State University of New York (Coursera)

Taught by Tural Sadigov and William Thistleton of SUNY, this is the statistics-first route: stationarity, autocorrelation, moving-average and autoregressive processes, Yule-Walker equations and the PACF, model selection with AIC, and integrated models, finishing with seasonality, SARIMA and forecasting in the sixth module. The course is taught in R, using real series such as rainfall, sunspot activity and stock prices. It has 4.6 stars from 1,742 reviews and 99,277 learners, and Coursera estimates about three weeks at ten hours a week. It is the best way to understand what the ARIMA function in a library is actually doing, which is what separates forecasters from people who call fit().

Best for: learners who want the statistical foundations and a certificate.  Worth knowing: it is included in Coursera Plus ($319.20 a year, normally $399, or $59 a month); you can enroll free to preview it first.

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3. Sequences, Time Series and Prediction — DeepLearning.AI (Coursera)

Laurence Moroney’s course is part of DeepLearning.AI’s TensorFlow Developer Professional Certificate and the clearest introduction to neural network forecasting: preparing time series data for learning, then recurrent networks and 1D convolutional networks for prediction, finishing with a sunspot-prediction model built on real-world data. It is rated 4.7 from 5,166 reviews, with 152,168 enrolled, and takes about two weeks at ten hours a week. It is deliberately practical rather than statistical, so it pairs well with #2.

Best for: Python developers who want deep learning forecasting in TensorFlow.  Worth knowing: it will not teach you ARIMA or why simple baselines often win; read the next section before you trust an LSTM.

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4. DataCamp’s Time Series in Python track — the guided on-ramp

DataCamp’s track is about 20 hours of short, in-browser lessons on manipulating, visualizing and modeling time series in Python with pandas, NumPy and Matplotlib, and it was updated in September 2026. It is the gentlest structured start: you write code in the browser and get instant feedback, and you can start the track free. Its ceiling is depth, which is what the lead pick supplies once you are fluent.

Best for: beginners who are still building pandas fluency.  Worth knowing: full access needs a DataCamp subscription.

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5. Python for Time Series Data Analysis — Udemy (Jose Portilla)

Portilla’s course is the most-reviewed time series course on Udemy (4.7 stars from 9,286 ratings and 52,710 students) and covers pandas date handling, statsmodels, ARIMA, SARIMA and SARIMAX, recurrent neural network forecasting and Facebook’s Prophet library across 15.5 hours. The honest caveat: it was last updated in July 2020, so some library calls are dated and it predates the newer forecasting libraries. Reddit commenters still recommend it, alongside the Lazy Programmer course, for getting your feet wet.

Best for: beginners who like Portilla’s teaching style.  Worth knowing: not updated since 2020; the lead pick is current.

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6. Kaggle Learn: Time Series — free

Kaggle’s free Time Series course takes about five hours and treats forecasting as a machine learning problem: linear regression with time steps and lags, modeling trend with moving averages, seasonality with indicator and Fourier features, using time series as features, hybrid models, and multi-step forecasting. Each lesson ends with an exercise in a hosted notebook. It is the fastest free way to see how forecasting looks in a competition setting.

Best for: people who already know scikit-learn.  Worth knowing: no statistical theory, and the certificate is informal.

7. Forecasting: Principles and Practice — the free foundations

Rob Hyndman and George Athanasopoulos’s textbook is the field’s standard reference and is free online in full. The R edition (FPP3) has been the default answer on Reddit for years: in every forecasting thread we read, it was the first recommendation. Its long-standing weakness, being R-only, is now fixed by Forecasting: Principles and Practice, the Pythonic Way, which follows the same chapters and adds chapters on neural networks and foundation forecasting models. It is unmonetized for us; on merit it belongs on every forecaster’s desk.

8. MIT 14.384 Time Series Analysis — free graduate course

For the mathematical depth that economists and quantitative finance roles expect, MIT’s graduate 14.384 (Fall 2013) is free on OpenCourseWare with lecture notes, readings, assignments and exams. It covers the theory and application of time series methods in econometrics, including univariate stationary and non-stationary models, vector autoregressions and frequency-domain methods. It is old and dense, but nothing here changes quickly at that level.

Classical vs ML forecasting: what courses should teach (and often don’t)

The forecasting competitions run by Spyros Makridakis are the best public evidence on this, and they point both ways. In the M4 competition (2018, 100,000 real series), 12 of the 17 most accurate methods were combinations of mostly statistical approaches, the winner was a hybrid of exponential smoothing and a neural network, and the six pure machine learning entries performed poorly: none beat a simple statistical combination benchmark. In M5 (2020, Walmart sales data on Kaggle), the picture flipped: the top entries were pure machine learning approaches, with LightGBM gradient-boosted trees prominent, all beating the statistical benchmarks.

The lesson is not that one camp wins. Short, single series with little extra information usually favor statistical models; large collections of related series with rich features (promotions, prices, calendars) favor machine learning. A trustworthy course teaches ARIMA and exponential smoothing first, then shows where gradient boosting and neural networks earn their complexity, which is exactly the structure of the lead pick. Courses that jump straight to LSTMs are teaching you to lose gracefully to exponential smoothing.

The working forecaster’s workflow (what good courses drill)

• Plot and decompose first. Trend, seasonality and holidays; plotting before modeling catches half of all problems.
• Always run a naive baseline. Last value or seasonal naive. If your model cannot beat “same as last Tuesday,” it is decoration.
• Backtest with rolling origins. Random train/test splits leak the future; this is the classic beginner mistake.
• Quantify uncertainty. A point forecast without prediction intervals is a guess; business decisions live in the intervals.
• Monitor drift. Every series eventually breaks its own history; 2020 made that permanent.

Interviewers probe exactly these five, and courses that skip backtesting methodology are the ones to avoid.

The Python libraries you will use

In R, the fable family used in FPP3 covers the same ground. Reddit practitioners recommended Darts and Nixtla repeatedly, with the caveat that Darts’ “made easy” claim undersells its learning curve.

The main forecasting models, in plain English

You do not need all of these. Most working forecasters lean on three or four: a naive baseline, exponential smoothing or ARIMA for single series, and gradient boosting once they have many series and good features. The lead course covers every row in this table except foundation models.

How do you know if a forecast is any good?

Accuracy has to be measured on data the model has not seen, using a time-ordered split. The common error measures each have a catch:

• MAE (mean absolute error): the average size of the miss, in the series’ own units. Easy to explain to a manager.
• RMSE (root mean squared error): punishes big misses more than small ones; useful when large errors are costly.
• MAPE (mean absolute percentage error): popular because it is a percentage, but it breaks when actual values are near zero and penalizes over-forecasts and under-forecasts unevenly.
• MASE (mean absolute scaled error): compares your error with a naive forecast’s; below 1 means you beat the naive baseline. FPP presents scaled errors as the alternative to percentage errors when you compare accuracy across series.
• Interval coverage: for prediction intervals, check how often the actual value falls inside them. A 95% interval that catches 70% of outcomes is overconfident.

A course that grades you only on in-sample fit, or on a random split, is teaching a habit you will have to unlearn.

Which time series course should you choose?

• You want one course that covers everything: #1, Lazy Programmer on Udemy.
• You want to understand the statistics and earn a certificate: #2, SUNY on Coursera (R).
• You are a TensorFlow developer: #3, DeepLearning.AI on Coursera.
• You are new to pandas: #4, DataCamp, then #1.
• You want free and rigorous: #7, FPP (pick the R or Python edition), plus #6, Kaggle, for practice.
• You work in economics or quantitative finance: #8, MIT 14.384, after #2.

Portfolio projects that prove time series skill

Employers trust a well-documented forecast more than a certificate. Three projects cover what interviewers ask about:

• A retail demand forecast: forecast weekly sales for many products, compare a seasonal naive baseline, ETS and LightGBM with rolling-origin backtests, and report MASE.
• An anomaly detector: flag unusual points in server metrics or electricity load, and explain how you chose the threshold.
• A forecast with uncertainty: produce prediction intervals for a public series (energy demand, tourism arrivals, air quality) and check their coverage on held-out data.

Write each up in a notebook with the baseline result first. Showing that you beat, or failed to beat, a naive forecast is the signal that you understand the field.

In what order should you learn time series?

• Step 1 (1 to 2 weeks): pandas date handling and plotting. DataCamp (#4) or the opening sections of Portilla (#5).
• Step 2 (2 to 4 weeks): the statistical core: stationarity, autocorrelation, ARIMA, exponential smoothing. FPP (#7) plus the SUNY course (#2).
• Step 3 (2 to 4 weeks): machine learning forecasting with lag features and gradient boosting. Kaggle (#6) and the ML half of the lead pick.
• Step 4 (optional): neural networks and foundation models. DeepLearning.AI (#3) and the newer FPP Python chapters.
• Throughout: pick one real dataset (your company’s sales, public energy load) and forecast it with each method against a naive baseline.

Should you learn time series in R or Python?

Both are first-class. R has the deeper statistical forecasting tradition: Hyndman’s forecast and fable packages, the FPP3 textbook and most academic econometrics material are R-first, and the SUNY course (#2) teaches in R. Python is where most data science jobs and machine learning tooling live: statsmodels, Prophet, sktime, Darts, Nixtla and the gradient-boosting and deep learning libraries, which is why most courses here (#1, #3, #4, #5, #6) use it.

If you are choosing for a job, pick Python, then read FPP’s Python edition so you get the statistical grounding without switching languages. If you work in economics, public policy, or a research team that already uses R, start in R. The concepts transfer cleanly either way; only the function names change.

Coursera vs Udemy vs free: which should you pick?

Choose Udemy if you want the most material per dollar and learn best by coding along; the lead course is the most complete single resource. Choose Coursera if you want a university or DeepLearning.AI certificate, graded assignments and a structured schedule; both Coursera courses are included in Coursera Plus. Go free if you are disciplined: FPP plus Kaggle covers more than most paid courses, but nobody checks your work.

Where time series skills pay

Demand planning and supply chain (the biggest employer of forecasters), finance and trading (see our coding for finance guide for the domain layer), energy and capacity planning, marketing-mix and web analytics, and machine learning engineering roles where anomaly detection guards production systems. The U.S. Bureau of Labor Statistics reports 2025 median pay of $120,230 for data scientists and $88,940 for operations research analysts, two roles where forecasting is core. In data science interviews, time series questions are a favorite precisely because many candidates skip the topic; our interview prep guide covers how they are asked.

What forecasters on Reddit recommend

• FPP first. Hyndman’s free book was the top recommendation in every r/datascience and r/learnmachinelearning thread we read, often with no other comment needed.
• ritvikmath for intuition. His YouTube time series playlist was praised for making the math intuitive.
• Udemy to get started. Commenters suggested Portilla’s or Lazy Programmer’s course for beginners, then the book.
• Shumway and Stoffer for depth. Time Series Analysis and Its Applications came up as the companion text for the mathematical side.
• Learn from competitions. Kaggle forecasting competitions were named as the place to see what works now.

FAQs

What is the best time series course?

Time Series Analysis, Forecasting, and Machine Learning on Udemy (4.6 stars from 3,156 ratings, updated March 2026) is the best single course because it covers classical statistics through deep learning with code. Hyndman’s free Forecasting: Principles and Practice is the best companion.

Is there a free time series course?

Yes. Forecasting: Principles and Practice is free online in R and Python editions, Kaggle Learn has a free five-hour Time Series course, and MIT OpenCourseWare publishes its graduate 14.384 Time Series Analysis course for free.

Should I learn ARIMA or machine learning for forecasting?

Both, in that order. In the M4 competition, combinations of mostly statistical methods dominated and pure ML entries did poorly; in M5, with large related retail series and rich features, gradient-boosted trees won. Learn the statistical core first, then ML.

What are the prerequisites for time series analysis?

Comfortable Python (pandas especially) and basic statistics: distributions, correlation and regression. If those are shaky, DataCamp’s Time Series in Python track builds them; the deeper courses assume them.

What is differencing in time series?

Differencing replaces each value with the change from the previous value. It removes a trend so the series becomes stationary, which ARIMA models require; seasonal differencing does the same across a seasonal lag, such as 12 months.

Is time series analysis in demand?

Yes. Demand planning, finance, energy and machine learning engineering all hire for it, and it is underrepresented among data science graduates, which makes it a genuine differentiator in interviews.

Is there a time series course with a certificate?

Yes. Coursera’s Practical Time Series Analysis from the State University of New York and Sequences, Time Series and Prediction from DeepLearning.AI both issue shareable certificates; Udemy courses issue a certificate of completion.

How long does it take to learn time series forecasting?

With solid Python and basic statistics, about two to three months of part-time study covers the core: one to two weeks of pandas date handling, a month on ARIMA and exponential smoothing, and a month on machine learning forecasting and backtesting. Deep learning methods add another few weeks.

Is time series forecasting machine learning?

It can be either. Classical forecasting (ARIMA, exponential smoothing) comes from statistics; machine learning forecasting reframes the problem with lag and calendar features and models such as gradient-boosted trees or neural networks. Good forecasters use both.

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