Time Series Forecasting - Part 5

ARIMA using SAS

We have covered basics about time series and also the basic methods of forecasting. It is time to learn the most important and most widely used for time series forecasting : ARIMA.

It is not possible to write ARIMA in a single stretch, it being full of complication, hence we plan to write it in series of article.

Time Series Forecasting - Part 4

Triple Exponential Smoothing - Excel : SAS : R

In the previous article of this series, we explained Double Exponential Smoothing method, also called Holt's method for time series forecasting. Now we are taking up TES also called Holt Winter's Method.
Once we are done with this article we are all set to learn ARIMA.

Time Series Forecasting - Part 3

Double Exponential Smoothing - Excel : SAS : R

In the previous article of this series, we described Single Exponential Smoothing method and its applicability.
Let's now learn the second method which is DES, its applicability and how to perform it in Excel, SAS and R.

Time Series Forecasting - Part 2

Single Exponential Smoothing - Excel : SAS : R

In the previous article of this series, we described basics about the time series and also enlisted the methods that can used for time series forecasting.
Let's now learn the methods one by one in details.

Time Series Forecasting - Part 1

Not just ARIMA, but much more

"Time is money and hence time series is equivalent to a treasure!" ... No Einstein or Hawking has said that, it came to my very unpopular mind, while I started writing about time-series.
Ask Analytics is committed to cover the subject in best possible depth and breadth ever, so not just ARIMA, we will explain each and every aspect of time series in a series of articles.

Seasonality Index and Trend Variables

Think of a time-series and the first term that comes to our statistical mind is ARIMA. Right ?

We would cover ARIMA soon on our blog, but its applicability is limited to forecasting. Also, it is a uni-variate practice that doesn't consider external factors. 

More often we need to study the effect of external factors on the a time-series such as sales, revenue etc. In such cases, we can use regression analysis while at the same time considering the key elements of time-series :  Seasonality and Trend.  Let's learn do we calculate these variables.