If you average over 5 periods (a so-called 5-MA), you use the two preceding periods, the current period, and the two following periods – meaning that you will have missing data for the first two periods and the last two periods of the time series. The first step in a classical time series decomposition is to estimate the trend by means of a centered moving average. multiplicative decomposition ( y t = T t x S t x R t) is appropriate when the seasonal fluctuations appear to be proportional to the level of the time series.additive decomposition ( y t = T t + S t + R t) is appropriate when the seasonal fluctuations appear constant over time.In time series decomposition, you single out a trend (or trend-cycle) component T and a seasonal component S what is left is the remainder component R. First let me recommend this free book, and this web page that I will follow almost literally. And note the sweet menu at the top-right corner of the graph. Title="Air passengers" file='line1.html'. !lineplot passengers / x=yearMonth xnumeric=0 Strings that are not hard-coded (like the title of the graph passed as an argument to the macro) need to be stored in every record, even if you need them only once. I use AGGREGATE with MODE=ADDVARIABLES to store the number of records in each record and then compare $CASENUM with that number. Likewise, for the stuff that follows the data, you need to know which is the last record. You can write all the stuff that needs to be written first (like the html code in the macro) by filtering DO IF $CASENUM EQ 1. WRITE writes the strings and variables you supply for every record in the active file. It does not integrate with pspp’s output but instead, with pspp’s WRITE command, creates a separate html file with code and data for a Vega-Lite graph. The closest you get is with a bar chart: GRAPH /BAR = MEAN(passengers) BY yearMonth. When working with time series you want to look at the data with line plots, which unfortunately PSPP does not sport at the moment. Some housekeeping for formats and to create a variable for the time series labels: VALUE LABELS month 1 'Jan' 2 'Feb' 3 'Mar' 4 'Apr' 5 'May' 6 'Jun'ħ 'Jul' 8 'Aug' 9 'Sep' 10 'Oct' 11 'Nov' 12 'Dec'.ĬOMPUTE yearMonth = CONCAT( STRING(year, F4.0), '-', STRING(month, N2.0)).įORMATS year (F4.0) month (F2.0) passengers (F3.0). Now transform this into long format with XSAVE: VECTOR m = Jan TO Dec. Don’t think I type in all those commands just like that! After almost every command I run PSPP with the syntax up to that point: In what follows, at times I show big chunks of syntax. year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec (F4.0). Series Analysis, Forecasting and Control._ Third Edition. International airline passengers, 1949 to 1960.īox, G. Monthly Airline Passenger Numbers 1949-1960 R version 4.2.2 () - "Innocent and Trusting"ĪirPassengers package:datasets R Documentation I will borrow it from R (note: “>” is the prompt inside R): $ R Like almost every article or post about time series, I will use the famous Air Passengers time series for this exercise. You learn a lot from doing things manually! As always, for every cloud a silver lining, or as the great Johan Cruijff would say: every disadvantage has its advantage. At the moment, the SPSS commands for handling time series have not been implemented in PSPP.
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