NumXL

Whether you have a simple homework problem or a large-scale business project, NumXL simplifies your efforts. It helps you reach your goal in the quickest, most thorough way possible.

NumXL keeps your data and results connected in Excel, letting you trace your calculations, add new data points or update an existing analysis, easily sharing your result with co-workers - and, yes, even with your boss.

The learning curve couldn't be easier: NumXL requires no programming or scripting. You will not have to move your data between any external programs.

You can also do any kind of ad-hoc analysis, as all of NumXL functions are accessible in your spreadsheet, and inside VBAⁱ environment should you choose to script.

Demo Videos        Getting Started        Reference Manual

       User's Guide       Case Studies

1. General Statistics

Using the descriptive statistics and correlogram wizards, you can examine the data series summary and time series statistics with just a few clicks. The wizards come with an extensive set of statistical tests, from a simple one-sample test for mean to the more sophisticated normality and ARCHⁱ effect tests.

Furthermore, examining time dependency (auto correlation) in your sample data is only a few clicks away.

The wizards generate professionally organized tables and graphs summarizing all your calculations, ready to be included in your presentation. To make things even easier, all outputs include NumXL functions in their formulae for connecting values with inputs, so you can edit, update or customize as you see fit. You can even re-run the wizard if you feel lazy.

Aside from the Wizards, NumXL has numerous functions for measuring prediction power (e.g. SSEⁱ, RMSEⁱ, MAPEⁱ and others).

To visualize your data distribution, NumXL offers several functions to ease the task of constructing histograms, empirical cumulative distribution (EDFⁱ), and kernel density estimate (KDEⁱ).

2. Correlogram Analysis

Using the correlogram wizard and function, constructing autocorrelation and partial autocorrelation plots is a snap.

Furthermore, NumXL comes with support for calculating cross correlation using three different methods: Pearson, Spearman and Kendall.

Finally, for completion purposes, NumXL support Hurst exponent analysis and GINI coefficient.

3. Statistical Testing           Demo Video

For serious data analysis, you might need to consider the statistical significance of a calculated parameter (e.g. autocorrelation factor, excess kurtosis, etc.) or verify an assumption from earlier data (e.g. Normality, stationarity, absence of serial correlation and others).

NumXL packages the majority of these tests as a simple API to calculate the P-Value of the underlying test.

5. Smoothing           Demo Video

Smoothing and filtering are two of the most commonly used time series techniques for removing noise from the underlying data to help reveal the important features and components (e.g. trend, seasonality, etc.). However, we can also use smoothing to fill in missing values and/or conduct a forecast.

NumXL supports several smoothing functions, from a simple weighted-moving average (WMAⁱ) to Winter’s triple exponential smoothing function.

Of course, we can’t talk about smoothing without mentioning trend functions. Trend analysis is very often used (or abused) in the industry to make a quick (and dirty) forecast. Executives might use the trending tool as a sanity check when he/she examines results from more advanced models. NumXL supports several forms of trend: linear, polynomial, power, exponential and logarithmic.

6. Calendar functionality           Demo Video

Calendar events influence the values of the time series sample, and a prior adjustment for those events will help us to better understand the process, modeling and forecast.

NumXL comes with numerous functions to support calendar adjustment, date rolling and adjustment, U.S. and non-U.S based holidays support, non-western weekends, and public and bank holiday calendars.

7. Spectral Analysis           Demo Video

In statistics, spectral analysis is a procedure that decomposes a time series into a spectrum of cycles of different lengths. Spectral analysis is also known as frequency domain analysis.

NumXL currently support the discrete Fourier transformation (and its inverse), with future plans for more extensive coverage.

8. ARMA/ARIMAⁱ           Demo Video

NumXL comes loaded with numerous functions to help you with any ARMA analysis task. You can start by specifying the model’s order using the ARMA wizard. The wizard will tie in model-related calculations – the Log-likelihood function, Akaike’s information criterion (AICⁱ) and residual diagnosis - with your input data. Once that is done, fitting the model parameters (calibration) is a snap: simply select the model and click on "calibration." The same goes for forecasting: select the model table and click "forecast".

You can always edit the formulas in the output cells, get intermediate calculations (e.g. residuals, fitted mean) or use a NumXL function in your own formulas or VBA code if you so choose.

9. Seasonal ARIMA           Demo Video

NumXL support seasonal ARIMA through two different models: (1) AirLine, and (2) X-12-ARIMAⁱ.

X-12-ARIMA is the widely used seasonal adjustment program developed, supported and maintained by U.S. Census bureau. NumXL provides an intuitive interface with the program to help Excel users make forecasts and seasonal adjustments quickly and efficiently for economic and financial data.

NumXL also offers users access to all raw files (input/output) that are generated in the process of the data analysis.

10. ARCH/GARCHⁱ           Demo Video

Similar to ARMA/ARIMA, modeling a GARCH-type model is a breeze. Using the GARCH Wizard, you can generate a model output table with all coefficient values and related calculations (e.g. LLFⁱ and residual diagnosis). This table can be used to calibrate the model and predict out-of-sample values.

NumXL also supports ARCH/GARCH, exponential GARCH (EGARCHⁱ) and GARCH-in-mean (GARCH-Mⁱ) models natively.

Furthermore, NumXL supports Gaussian, Student's t and GEDⁱ-type innovations.

11. ARMA-GARCH mixture model

In the event you wish to model the time-varying conditional mean and the conditional volatility in one model, an ARMA-GARCH mixture model is in order. By combining the two models, the ARMA will follow the mean and pass the residuals to GARCH to follow the variance over time.

ARMA-GARCH combo models support all the ARMA and GARCH models supported solo, including the non-Gaussian innovations.

12. Generalized Linear Model (GLMⁱ)           Demo Video

Whether you have a logistics regression or a general linear model in mind, you can use the generalized linear model (GLM). NumXL uses the GLM wizard to help you specify the input data, link functions (e.g. probit, logit, log complement) and generate a model output table.

As with all of our models, the output model table is used to calibrate and perform out-of-sample forecasts.

13. Miscellaneous          Demo Video

Throughout our analysis, we implemented a few functions that did not fit in with any of those listed above: interpolation, extrapolation, least squares regression and regular expressions functions. We combined these orphan functions into a single category called “Utilities”.

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