XLMiner is a data mining add-in for Microsoft Excel that provides tools for time series analysis. There are two exploratory techniques used in XLMiner for time series analysis: the autocorrelation function (ACF), which finds the correlation between observations separated by a time lag, and the partial autocorrelation function (PACF), which measures the strength of relationships between components while accounting for other components. It is important to partition the time series data into training and validation sets to validate that the time series pattern is consistent and the model is accurate enough for prediction purposes.

1. Introduction toXLMiner™The Data mining add-in for Microsoft Excel.TIME SERIESXLMiner and Microsoft Office are registered trademarks of the respective owners.

2. TIME SERIESTime series analysis is done to understand the distribution of data points over time and such an analysis is useful for the purpose of prediction – for example, the future trends over time can be predicted by information extracted from the past performance. In XLMiner, there are two exploratory techniques used for time series analysisACF (Autocorrelation Function):Autocorrelation is the correlation between observations of a time series separated by say, k time units. Suppose there are n time based observations, in ACF technique XLMiner�finds correlation between the observations for different lags.PACF (Partial Autocorrelation Function):PACF technique is used to compute and plot the partial autocorrelations of a time series. With PACF we can find correlation between some components of the series, eliminating the contribution of other components. PACF technique measures the strength of relationship with other terms being accounted forhttp://dataminingtools.net

3. TIME SERIES- Partitioning DataIn order to find if the time series created using XLMiner is accurate enough to proceed, we need to partition the data set into training and validation partitions and then see if the time series pattern is same for both. If the difference is too much we need to train our model againhttp://dataminingtools.net

4. TIME SERIES- ACFhttp://dataminingtools.net

5. TIME SERIES- Partitioning Datahttp://dataminingtools.net

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