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Simple Time Series Models as primitive forecast methods

Let's start with what is Time Series data. According to the famous book of Rob J Hyndman "Forecasting: Principles & Practice" it is a sequences of observations collected over time. One of the characteristic of Time series is that there is successive order for values in opposite to vector where a unique ID doesn't necessarily provide a specific order to the data. Forecasting however is estimating how the sequence of observations will continue into the future according to the book of Hyndman.

As we can imagine we can find Time Series data in various sector of business life and managing people would like to know what we can expect in the nearest future. It is crucial because by knowing in advance that something is coming we can prepare better and omit some losses. 

R program has develop Time Series forecasting pretty well. We can build advanced ARIMA models as well as Exponential Smoothing or even Neutral network models and more. Today I would like to focus on some basic primitive methods like Mean method, Naïve method, Seasonal naïve method, Drift method which works well with random data set. You can use this methods with library 'forecast' so it is better to install it right away. What is it all about?

Mean Method - returns the mean as forecast value with function meanf()

Naïve Method - returns the last observation as forecast value with function naive()

Seasonal Naïve Method - returns the last observation of the seasonal stage with function snaive()

Drift Method- carries the change over first and last observation into the future with function rwf()

Let's see how to use it now in practice. We will start with using simple time series methods on some random data set and most of the time those method are helpful in this type of data where there is no trend, no seasonality and other statistical characters of data.

library(forecast)
set.seed(60) # We want to make results reproducible

We are taking 400 randomly distributed numbers. Time series starts in Q1 of 1900 and it is quarterly data.

randomtseries= ts(rnorm(400),start=c(1900,1),frequency=4)
plot(randomtseries)

It is how random distributed data looks like. There is no particular trend, no seasonality. Now we are defining our 3 models by using functions I mentioned above. Model forecast will have 15 observations (parameter h).

meanrtsmodel<-meanf(randomtseries,h=15)
naivertsmodel<-naive(randomtseries,h=15)
driftrtsmodel<-rwf(randomtseries,h=15)

Now, each object contain the original data and 15 forecast values. We can plot original data and our " models in the same graph.



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