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Bringing Inovative Time Series Analysis To You Quickly & Efficiently

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What can NumXL do for me?

Transform Excel into a first-class time series software and econometric tool. Use accurate statistical calculations comparable to ones offered by other elite statistical packages. Apply scores of econometric functions, a rich set of shortcuts, and intuitive user interfaces to guide you through the entire process.

Descriptive Statistics

Descriptive Statistics

Draw summary statistics and conduct powerful tests like Normality, White Noise, Cointegration and more.

Data preparation

Data preparation

Resample, transform, and fit your data to help you find the inslights you need.

Smoothing

Smoothing

Apply sophisticated smoothing techniques to extract various components in your data using Exponential Smoothing.

Factor Analysis

Factor Analysis

Investigate multidimensional data sets to reduce or establish a relationship with Principal Component Analysis (PCA) and Multiple Linear Regression (MLR).

Forecasting

Forecasting

Capture the dynamics and volatility of your data, and forecast future values with confidence.

Spectral Analysis

Spectral Analysis

Break down your data into its components using Fourier Transform and filters like Hodrick-Prescott and Baxter-King.

21 Sep

Smoothing

Discuss five (5) different smoothing methods: weighted moving average (WMA), simple exponential smoothing, double exponential smoothing, linear exponential smoothing, and triple exponential smoothing.

14 Sep

Volatility 102

In this issue, we start by defining the various terms in an asset’s return time (e.g. holding period) and explain in detail the multi-period forecast of returns and volatility. Next, we discuss the scaling issue with volatility computed with different holding periods and establish a common... Read more

14 Sep

Volatility 101

This is the first entry in what will become an ongoing series on volatility modeling. In this paper, we will start with the definition and general dynamics of volatility in financial time series. Next, we will use historical data to develop a few methods to forecast volatility. These methods... Read more

Our Blog

14 Sep

The Ins and Outs of Histograms

In this issue, we will tackle the probability distribution inference for a random variable. we’ll start with the non-parametric distributions functions: (1) empirical (cumulative) density function and (2) the histogram. In later issue, we’ll also go over the kernel density... Read more

14 Sep

Patterns Unplugged

This is the third issue in our ARMA Unplugged modeling series. In this issue, we introduce the common patterns often found in real time series data and discuss a few techniques to identify/model those patterns, paving the way for more elaborate discussion decomposition and seasonal... Read more

13 Sep

Homogeneity

In this paper, we explore one of the fundamental assumptions of data preparation in time series analysis: "homogeneity," or the assumption that a time series sample is drawn from a stable/homogeneous process. We’ll start by defining the homogeneous stochastic process and... Read more