Machine Learning Time Series Course at Wisen IT Solutions, Chennai, India, develops practical forecasting skills for the series businesses plan against — demand, sales, load and traffic. Learn pandas time handling, decomposition and stationarity, ACF and PACF reading, exponential smoothing, ARIMA and SARIMA, lag-feature machine learning, backtesting, accuracy metrics and forecast monitoring through project-focused, AI-Assisted Learning.
Call +91 900 31 31 555AI-Enabled Career-Focused Time Series Course. Build Forecasts That Hold Up in Production.
Wisen IT Solutions, Chennai, India
Think with AI. Don't Depend on AI.
Decompose. Forecast. Backtest.
Build practical forecasting skills for the AI-Era through hands-on training in time series handling with pandas, decomposition and stationarity testing, ACF and PACF reading, exponential smoothing and ETS, the ARIMA and SARIMA family, machine learning with lag features, expanding and sliding window backtesting, forecast accuracy metrics, multivariate and state space models, and deploying and monitoring a forecast. Learn to build validation that respects time, develop project-ready, AI-ready forecasting skills, and open new career opportunities in demand planning and analytics.
What Makes Time Series Different Topics
What Makes Time Series Different Topics
Moving Beyond
Traditional Training
with
AI-Enabled Learning.
AI-Ready Technology
Learning Lab
Moving Beyond
Traditional Training
with AI-Enabled Learning
For Organizations
Build forecasting capability through AI-Enabled Learning across decomposition, stationarity, the ARIMA family, lag-feature machine learning, backtesting and forecast monitoring. Wisen’s Time Series Training combines AI-Assisted Learning for the concepts with AI-Paired Training for practical backtesting, so your team stops reporting a validation score that quietly used tomorrow to predict today.
Develop skills aligned with the demand, load and revenue planning teams actually own.
Use AI to accelerate backtest review without replacing statistical judgement.
Structured paths for new hires and for analysts moving from reporting into forecasting.
Practise decomposition, order selection, expanding-window backtests and error tracking.
Align the syllabus with your series, your horizon and your planning cycle.
Evaluate practical progress through AI-assisted assessments that catch look-ahead bias.
Develop practical forecasting capability, learning to understand a series before modelling it, choose between classical and machine learning approaches on evidence, and design a backtest that cannot flatter you by using information the model would not have had.
Work with DatetimeIndex, frequencies, resampling and time zones without silent gaps.
Build rolling, expanding and lagged features that respect the time boundary.
Separate trend, seasonality and remainder with classical and STL methods.
Test with ADF and KPSS, difference appropriately, and recognise over-differencing.
Read correlation plots to propose sensible model orders rather than guessing.
Apply simple, Holt and Holt-Winters methods, and know when they are enough.
Specify, fit and diagnose the ARIMA family including seasonal and exogenous terms.
Reframe forecasting as supervised learning with lag and calendar features.
Choose between direct and recursive strategies for a horizon beyond one step.
Run expanding and sliding window backtests across multiple forecast origins.
Recognise look-ahead bias in features, splits and preprocessing.
Use MAE, RMSE, MASE and pinball loss, and know why MAPE fails near zero.
Produce intervals and check whether their coverage is actually honest.
Use AI to accelerate diagnosis while validating every claim against your own backtest.
Track error over time, detect drift and decide when to retrain.
After completing the training, participants can analyse a series, choose and fit an appropriate model, backtest it without leaking the future, quantify its uncertainty, and monitor its accuracy once it is in production.
Most training providers set their own test and mark their own paper. We do not. At the end of each stage of the Machine Learning Time Series Training you check your own readiness using your own ChatGPT, Claude, Gemini or other AI account. Wisen does not write the questions, does not see your answers, and does not record your score.
The reasoning is straightforward. A score we control proves very little — to an employer, or to you. A score produced by a tool we have no influence over is worth something. You ask the AI to test you on Machine Learning Time Series, it decides what to ask, and the result belongs to you alone.
Seventy per cent is the mark we treat as ready. Score seventy or above and you move on to the next stage. Score below it and we work through the gap with you: identify what was missed, teach it again, practise it, then go back to the AI and check. You repeat that loop as many times as it takes.
Time series training for people who have been burned by a random split. Jobs are not guaranteed; not leaking the future is.
Stationarity, seasonality, backtesting with an expanding window and knowing why a random split is meaningless here are exactly what two years of forecasting teaches.
Get expert guidance to build a strong, professional resume that highlights your skills, projects and achievements.
Build real-world projects and a strong portfolio that demonstrates your practical skills to potential employers.
You will be asked why your backtest looked perfect and production did not. We rehearse the leakage answer and the seasonality one.
Forecasting roles sit in demand planning, finance, energy and operations. We help you target the sector whose data you can already reason about.
We assist you in identifying relevant opportunities and connecting with potential employers.
Your learning, projects and skills are verified by our Independent AI Verification System to ensure objective and unbiased evaluation.
Statistical and deep forecasting approaches both rest on the same evaluation discipline. You learn the discipline, so either family is approachable.
We guarantee no placement. Your result depends on your practice, your assessments, your interviews and the employer’s need. Our support does not stop at the last session.
The learning materials for this Time Series Course are developed from 27+ years of Python and data training experience, refined across classroom batches, corporate Machine Learning Training programs, learner questions, lab reviews, and forecasts built for real client projects.
Every chapter, notebook, lab activity and exercise in the Time Series Online Course is built on series that behave like real ones — a trend that changes slope, a seasonality that shifts, a promotional spike that is not noise, and at least one feature that will leak the future if you are not careful.
Structured explanations of stationarity, the ARIMA family and backtesting, written for study after each session.
Walkthrough labs that take one series from exploration to a backtested, monitored forecast.
Independent tasks on differencing, order selection, lag features and interval coverage.
Demand, load and sales data with trend changes, shifting seasonality and missing periods.
Topics sequenced from time handling to monitoring, so each session builds on the one before.
Quick reference for stationarity tests, ARIMA notation and accuracy metrics.
Written by trainers who taught statistical forecasting long before ML was a job title.
Created and maintained by practising trainers, not assembled from generated text.
Not copied from library documentation, books, or generic online forecasting tutorials.
Every concept arrives with a series to model and a backtest to run.
Updated as the library evolves, covering current APIs and flagging what is deprecated.
AI supports grammar, readability and presentation; the teaching content stays human.
Our Commitment
The time series topics listed on this website reflect the actual learning journey delivered in our live instructor-led online sessions. We follow the published sequence and enrich it with extra leakage case studies, backtest walkthroughs and forecast post-mortems whenever they help the batch. Whether you join a Machine Learning Training in Chennai batch or attend from elsewhere, the published order is what gets taught.
Time Series Training is evaluated twice over, independently. An experienced trainer assesses how you reason about time and evidence, and an independent AI evaluation reviews the code you write, so you learn where your backtest is dishonest as well as where your syntax is wrong.
Forecasting is the area where leakage is easiest to commit and hardest to spot: one careless split, one feature computed over the full series, and your backtest becomes fiction. This course puts both evaluations to work on exactly those failures.
Our experienced trainers evaluate your ability to:
Describe a series’ trend, seasonality and irregularities before modelling it.
Interpret ADF and KPSS together, including when they disagree.
Propose an ARIMA order from ACF and PACF rather than from a search alone.
Build expanding or sliding window validation that mimics real deployment.
Identify every route by which future information could reach the model.
Choose an accuracy metric that matches the planning decision.
Produce intervals and check their coverage honestly.
Explain a forecast and its confidence to a planner who will act on it.
Say plainly when a series is not forecastable at the horizon requested.
Our independent AI evaluation reviews your forecasting code to assess:
Verify correct use of statsmodels, resampling and lag construction.
Analyse whether the backtest supports the accuracy claimed.
Evaluate adherence to the conventions taught in the course.
Review readability, reproducibility and index handling.
Identify shuffled splits, full-series scaling and future-derived features.
Flag needless refits across backtest origins.
Recommend improvements based on modern forecasting standards.
Evaluate whether the notebook would survive a peer review.
Human trainers evaluate how you reason about time and defend your backtest design.
AI independently reviews the code for look-ahead bias, index errors and reproducibility.
Together they separate a backtest that looked perfect from a forecast that will hold.
By combining Human Evaluation with Independent AI Evaluation across our Machine Learning Training in Chennai and online, you will:
Machine Learning Training in Chennai and online is delivered in live batches with two pace options, so the schedule bends around your commitments instead of competing with them.
Total Learning Hours
50 - 55 HoursInstructor-led concept sessionsDecomposition and stationarity labsBacktesting and metric labsEnd-to-end forecasting project
A balanced rhythm that leaves time to rerun every backtest between sessions.
A concentrated schedule for learners who want the Time Series Online Course finished sooner.
Live Instructor-Led Training
Concept Deep Dives
Hands-on Forecasting
Backtesting Lab Activities
Seasonality Workshops
AI-Assisted Learning
Independent Code Evaluation
Doubt Clarification
Forecast Project Guidance
The Time Series Course content is identical on both tracks — the course you join in Chennai or online differs only in how quickly the sessions arrive.
Every session is taught live by a practising engineer — never a pre-recorded video. Batches run to Indian Standard Time, and the timing is adjusted to suit your time zone wherever you are.
You write code during the session, ask questions as they come up, and have that code reviewed.
Weekday and weekend slots in IST. If none of them suit where you live, we schedule a batch that does.
International debit and credit cards, PayPal and direct bank transfer are all accepted.
Look-ahead bias only teaches you something once a perfect backtest has failed in production. This Time Series Course therefore runs on a 40:60 Lecture-Practical Ratio, so every concept is immediately tested against a series where the careless approach visibly cheats.
Across the Time Series Training you decompose, difference, fit, backtest and monitor yourself. Nothing in the course is left as something you only watched someone else type.
Understand why order changes everything about validation.
Apply every concept to a real series in the same session.
Learn why yesterday predicts today before you model either.
Each concept is fitted against a live series in class.
A leaking backtest is met in the lab, not in a planning cycle.
Build the habit of validating across many forecast origins.
Leave able to deliver and defend a forecast end to end.
Every forecasting concept is followed by a backtest you have to justify.Wisen IT Solutions, Chennai runs Machine Learning Training in Chennai and online on the belief that forecasting is learned by being caught leaking the future in a lab, not by reading about stationarity.
This Time Series Course assumes working Python, comfort with pandas, and the modelling fundamentals — validation, leakage awareness and error metrics. If you have those, this course goes straight to what changes once observations are ordered in time.
The Time Series Training is delivered live online, so the Machine Learning Training in Chennai batch and the online batch start from exactly the same first series.
Data Analysts & Scientists
Demand & Supply Planners
Finance & Revenue Analysts
Anyone who has finished our Machine Learning Fundamentals course
If validation design and leakage are unfamiliar, start with our Machine Learning Fundamentals Course first. We will tell you honestly which of the two suits you — time series punishes weak validation habits harder than any other problem type, so it is worth having them first.
All you need is working Python, the modelling fundamentals and a series someone plans against.
We’ll take care of the rest!This Time Series Course works through both traditions in depth: the statistical models in statsmodels and the lag-feature machine learning approach, along with the diagnostic tests and backtesting machinery most tutorials skip entirely.
The Time Series Training works on series where seasonality shifts and trends break, which is what makes it an advanced course. Training in Chennai and the online batches run identical labs.
DatetimeIndex
resample & asfreq
Rolling & Expanding
shift, diff & pct_change
Time Zones & Calendars
seasonal_decompose & STL
ADF & KPSS Tests
ACF & PACF
Exponential Smoothing & ETS
ARIMA, SARIMA & SARIMAX
VAR & State Space
Lag & Rolling Features
Fourier Seasonality Terms
Gradient Boosting
Direct & Recursive Strategies
Global Multi-Series Models
TimeSeriesSplit
Expanding & Sliding Backtests
MASE & Pinball Loss
Interval Coverage
Drift Monitoring
Retraining Strategy
By the end of this Time Series Course you build backtests that cannot cheat, and you can explain why a perfect validation score was fiction — the difference an advanced forecasting course is meant to make.
Decompose a Series
Specify a Model
Backtest Honestly
Quantify Uncertainty
Stationarity tests, the ARIMA family and seasonal decomposition are taught from the project’s own time-series reference, with scikit-learn behind the validation work.
The reference for the ARIMA family, seasonal decomposition and the diagnostic tests.
The reference for estimators, pipelines, model selection and the evaluation metrics.
The library’s own reference for Series, DataFrames, indexing, grouping and reshaping.
You can learn forecasting by first understanding what makes ordered data different — dependence, trend and seasonality — then learning to backtest correctly, and only then collecting models. Starting with ARIMA is the common mistake; it produces people who can fit a model and cannot tell whether their validation was honest.
For most learners, a structured Time Series Training course provides a clearer path than assembling knowledge from scattered tutorials, because backtesting discipline has to be taught deliberately.
At Wisen, the Machine Learning Fundamentals Course is the recommended prerequisite for Time Series Training.
A practical learning progression can include:
Because a random split lets the model train on Wednesday and Friday and then predict Thursday. In a series with any autocorrelation at all — which is every series worth forecasting — that is close to giving it the answer.
The result is a validation score far better than anything production will see, and the failure only becomes visible weeks later when the real forecasts miss. Time-ordered splits and expanding-window backtests exist precisely to prevent this, and the course treats getting them right as more important than the choice of model.
For a single series with clear trend and seasonality and a few years of history, the classical models are usually as good and much easier to explain. ARIMA and Holt-Winters remain hard to beat on that shape of problem.
Machine learning with lag features earns its place when you have many related series, useful external drivers such as price or promotions, or non-linear effects the classical models cannot express.
The course teaches both and, more importantly, teaches you to compare them on the same backtest — because the honest answer is frequently that the seasonal naive baseline was already good enough.
MAPE divides by the actual value, so it is undefined when the actual is zero and explodes when it is near zero. On intermittent demand — a product that sells nothing on many days — it is not just noisy, it is meaningless.
It is also asymmetric: it penalises over-forecasting more heavily than under-forecasting, which quietly biases whoever is optimising against it. MASE compares your error to a seasonal naive baseline, works across series of different scales, and does not break near zero — which is why the course prefers it.
Yes. A course completion certificate is issued once you finish the assessments and the forecasting project.
It records that you completed the course and the project. It is not a vendor or university certification, and we would not suggest otherwise. What carries weight in an interview is the project and your ability to explain why your backtest can be trusted.
Product companies, global capability centres and services firms in Chennai all run teams on this stack, which keeps steady demand behind the Machine Learning Time Series Course. Because the statsmodels Time Series Course is used both for new builds and for long-lived systems already in production, that demand spans startups and established engineering organisations.
Yes. Freshers and graduates from non-IT streams join regularly, and Time Series Online Training starts from the fundamentals before moving into advanced work. If your programming basics are thin we will say so before you enrol, and suggest the order in which to take Time Series Forecasting Training and its prerequisites.
We run a one-week evaluation instead of a one-hour demo class. A ₹2,000 registration fee reserves your seat in the Machine Learning Time Series Course; attend the first week of live sessions and, if the training is not the right fit, that registration fee is refunded in full. If you register but cannot attend, it converts to Training Credit valid for 12 months. Once the course fee is paid, Time Series Online Training fees are transferable once, within a month, if you leave before two weeks, and are neither refundable nor transferable after two weeks of attendance.
We do not promise a job, and you should be wary of anyone who does. What Time Series Forecasting Training includes is preparation: we help you prepare your biodata, build a portfolio from work you have actually done, and identify the knowledge gaps that would cost you an interview. That review covers the the Machine Learning Time Series Course skills interviewers ask about today and the ones worth learning next, so you can keep preparing after the course ends.
Corporate Training Clients
The software industry is evolving with AI—not disappearing. Wisen's AI-Enabled Learning helps you master modern technologies, build strong engineering fundamentals, and collaborate effectively with AI tools like ChatGPT and Claude. Develop the practical skills, critical thinking, and real-world experience needed to build software with confidence and remain valuable throughout your career.
Talk to our AI Learning Advisor
+91 900 31 31 555