![]() ![]() Modelįit two theta lines to a deseasonalized time series, using different techniques to obtain and combine the two theta lines to produce the final forecasts. These models exploit the existing autocorrelations in the time series. These tools are useful for large collections of univariate time series. ![]() □️ Exogenous Regressors: like weather or prices Models Automatic ForecastingĪutomatic forecasting tools search for the best parameters and select the best possible model for a series of time series. □ Intermittent Demand: forecast series with very few non-zero observations. ![]() □ Predict Demand Peaks: electricity load forecasting for detecting daily peaks and reducing electric bills. ❄️ Multiple Seasonalities: how to forecast data with multiple seasonalities using an MSTL. □□ Cross Validation: robust model’s performance evaluation. □ Anomaly Detection: detect anomalies for time series using in-sample prediction intervals. □ End to End Walkthrough: Model training, evaluation and selection for multiple time series Missing something? Please open an issue or write us in Examples and Guides Fit 10 benchmark models on 1,000,000 series in under 5 min.Replace FB-Prophet in two lines of code and gain speed and accuracy.Compiled to high performance machine code through numba.Inclusion of exogenous variables and prediction intervals for ARIMA.Support for exogenous Variables and static covariates.Probabilistic Forecasting and Confidence Intervals.Out-of-the-box compatibility with Spark, Dask, and Ray.Fastest and most accurate implementations of AutoARIMA, AutoETS, AutoCES, MSTL and Theta in Python.StatsForecast includes an extensive battery of models that can efficiently fit millions of time series. So we created a library that can be used to forecast in production environments or as benchmarks. Why?Ĭurrent Python alternatives for statistical models are slow, inaccurate and don’t scale well. Failure to complete the course in 6 months and/or inactivity for 3 months will result in course access revocation.From statsforecast import StatsForecast from statsforecast.models import AutoARIMA sf = StatsForecast( models =, freq = 'M' ) sf.fit(df) sf.predict(h = 12, level =)įollow this end-to-end walkthrough for best practices. Note to SPARTA scholars: Upon enrollment, you will have 6 months to finish a SPARTA course. If the version of MS Excel is 2011, download and install StatPlus. For MacBook: ideally MS Excel 2013 or newer should be installed (some functions require this version on the Mac).For Windows: Core i3 or better, 4GB RAM or better, MS Excel 2007 or better.You will need a computer or laptop with Microsoft Excel installed. use Excel’s What-If functionality to perform simulations, sensitivity analysis, and optimization that would support recommendation of preferred course of action.leverage powerful statistical functions and add-ins of Excel to test business hypotheses or to make forecasts or predictions and.aggregation-based analysis, descriptive statistics, profiling, and segmentation) using Excel’s basic functionalities better understand what has happened to a business or organization, based on historical data, by performing foundational analytic techniques (e.g.Upon completion of this course, the learners are expected to: Participants will be trained using a case-based approach with either real or simulated data sets, to solidify their knowledge so they can apply the techniques that they have learned to their own work. Topics covered include descriptive, diagnostic, predictive, and prescriptive analytics. This course will teach participants how to perform the most important and commonly used analytics and modeling techniques in Excel. Hence, it is a valuable skill to learn in business or academia. Statistics transforms data into meaningful information, enabling organizations to make better decisions and predictions. ![]()
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