arima time series forecasting

series analysis was applied to economic forecasting before the Second World War. During and after the Second World War, it was widely used in military science, space science, industrial automation, and other sectors. In terms of mathematical methods, the statistical analysis of a stable random sequence (see the steady process) is developed in theory to form the basis of

that the OLS method is not very accurate, as far as possible or use the former. 4. A demo with R function In fact, all of our above, R language is ready, a very short code can be achieved. We use a 2-order autoregressive model as an example to illustrate. Model: YT = 0.7 * yt-1-0.5 yt-2 + C Again, C is the error term. y2 = Arima.sim (n = 100,list (AR = C (0.7,-0.5))) plot (y2,type = ' o ') pacf (y2) $ac [1:5]We can see the time

1 White noise process:0 mean, same variance, no autocorrelation (covariance is 0)in the future we encounter the Efshow if not specifically described, is the white noise process. for normal distribution, irrelevant can be introduced independently, so if the white noise is normal distribution, it will also be independent of each other. 2 various and modelsP-Order moving average process:Q-Order autoregressive process:Autoregressive Moving Average model:if the characteristic root of an arma (P,Q) mo

data can be analyzed in other ways, and we won't do it here.(3) Create a new mining structureRight-click on the mining structure, now create a new data mining structure, and then next ... Continue and Next ... Here do not repeat, do not understand can refer to the previous articles, we choose Microsfoft time Series algorithm, see figureClick Next, there are a few key points we need to set up, let's look at

The ARMAX model requires the use of R's DSE package, in the DSE package R, the ARMA model representation is general, so-VAR, Varx,arima, ARMAX, Arimax can all be co Nsidered to be special cases.The data set is natural gas (input) and generated CO2 (output) in the gas furnace, the data source is Wang application time series analysis of the third edition of the App

structure, now create a new data mining structure, and then next ... Continue and Next ... Here do not repeat, do not understand can refer to the previous articles, we choose Microsfoft time Series algorithm, see figureClick Next, there are a few key points we need to set up, let's look at the graph:Here we combine the brand and the region, the report date to form a key column, the sales and sales performa

... Continue and Next ... Here do not repeat, do not understand can refer to the previous articles, we choose Microsfoft time Series algorithm, see figureClick Next, there are a few key points we need to set up, let's look at the graph:Here we combine the brand and the region, the report date to form a key column, the sales and sales performance of two columns as input and as output, because these two colu

time is continuous. Of course, we can analyze the source data in other ways, we do not do here. (3) New mining structure Right-click on the mining structure, now create a new data mining structure, and then next ... Continue and then next ... Here do not repeat, do not understand can refer to the previous several articles, we choose Microsfoft time Series algori