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18

This site has exactly what you want here, already in Mathematica code. One example here:


13

Based on @b.gatessucks answer and on @RahulNarain comment tip, I created this functions for the multiplicative decompose case. I changed @b.gatessucks method for seasonality to keep it closer from R method, and used TemporalData to easily handle time interval. ...


13

The following seems a little more elegant. data = Import["http://www.massey.ac.nz/~pscowper/ts/cbe.dat"]; ts = TemporalData[data[[2 ;; -1, 1]], {"1958", Automatic, "Month"}]; DateListPlot[ts["Path"]] TemporalData can also store multiple paths. ts2= TemporalData[Transpose[data[[2 ;; -1]]], {"1958", Automatic, "Month"}]; DateListPlot[ts2["Paths"]]


11

So this generates the heatmap: << Calendar` year = 1990; yearLen = DaysBetween[{year, 1, 1}, {year, 12, 31}] + 1; data = RandomReal[1, yearLen]; days = Map[DayOfWeek[{year, 1, #}] &, Range[3, 9]]; day1 = Position[days, DayOfWeek[{year, 1, 1}]][[1, 1]]; dayn = Position[days, DayOfWeek[{year, 12, 1}]][[1, 1]]; Paddata = Join[ConstantArray[100, day1 ...


11

You could use RunScheduledTask or its relatives for this. For example, to append a random integer to catch once every two seconds you could do something like catch = {}; task = RunScheduledTask[AppendTo[catch, RandomInteger[10]], 2]; You could also use CreateScheduledTask which is similar to RunScheduledTask except that the task won't be started ...


11

One of the new operations on TimeSeries objects is TimeSeriesWindow. I think it does what you need. ts = WeatherData["KP60", "Temperature", {{2013, 7, 1}, {2013, 9, 30}}]; DateListPlot[TimeSeriesWindow[ts, {{2013, 8, 1}, {2013, 8, 14}}]]


10

There are basically two methods. The first is to use the ARProcess function (or ARMAProcess or ARIMAProcess as needed) introduced in version 9. The answers to this question should be helpful. Here is a small example that modifies the example in the documentation to show how to plot the resulting TemporalData using DateListPlot rather than ListPlot: you ...


8

The three best-known tests for stationarity (or rather, unit roots) in time series econometrics are: Dickey-Fuller including Augmented Dickey-Fuller Phillips-Perron KPSS There are also Bayesian tests of unit roots, as implemented in this conference presentation. If you have access to JSTOR or another way of getting at old journals, this article might be ...


8

Here are two approaches. We'll create a second dataset by shifting the given data by two months: blossom = {{4, 3}, {4, 22}, {4, 15}, {4, 2}, {4, 18}, {4, 20}, {4, 12}, {3, 30}, {4, 4}, {4, 24}, {4, 26}, {3, 4}, {4, 26}, {4, 13}, {5, 1}, {4, 4}, {4, 8}, {4, 18}, {4, 9}, {4, 19}, {4, 10}, {4, 20}, {4, 3}, {4, 4}, {3, 21}, {4, 19}, {4, 15}, ...


7

While you come back with a version 9 solution here is an old school approach : The first entry is labels so I removed it : rawData = Import["http://www.massey.ac.nz/~pscowper/ts/cbe.dat"][[2 ;;]]; Added the dates to the imported data : they are monthly dates starting {1958, 1, 1} : data = Transpose[{NestList[DatePlus[#, {1, "Month"}] &, {1958, 1, ...


7

Here's a line right out of the help for CellularAutomaton. ListLinePlot[Accumulate[(-1)^CellularAutomaton[30, {{1}, 0}, {500, {{0}}}]]]


7

The links provide you with everything you need I think. The goal of this answer is to show you that even though it's not built in, a discrete STFT is quite easy and short to code. This would take the DFT of the data set partitioned into chunks of length 2^13, with half a window overlap, and a rectangular window STFT[r_]:= Fourier /@ Partition[r, 2^13, ...


6

Wolfram has renamed the LogLikelihood function in the Time Series package to LogLikelihoodFunction. But they have apparently forgotten to update the documentation.


6

After some study, I think that I found out how to answer it using: data = TemporalData[salesData,{{2010,1},{2012,11},"Month"}]; proc=EstimatedProcess[salesData,SARIMAProcess[{},1,{},{12,{a},1,{b}},v]]; forecast=TimeSeriesForecast[proc, data,{14}]; DateListPlot[N@{data["Path"],forecast["Path"]} ,AspectRatio->0.2 ,Joined-> True ,PlotStyle ...


5

Straightforward approach with controlling related heights. Needs["HierarchicalClustering`"] SeedRandom@2; data = RandomVariate[NormalDistribution[], {10, 20}]; height = 50; label = ListPlot[#, Axes -> False, Joined -> True, ImageSize -> {300, height}, AspectRatio -> height/300] & /@ data; Edit I'm sorry my previous ...


5

Calling Wolfram|Alpha is not generally an efficient way to retrieve bulk data; where possible, it is better to use a built in data function. Part of the problem is figuring out what to submit to Wolfram|Alpha. In the code you supplied, the issue begins with Wolfram|Alpha returning Missing[NotAvailable]. WolframAlpha["AAPL ...


4

This looks like very useful function for e.g. economic time series. If this can be done easily in R does it need to be done in Mathematica? With the qualifier that this is my first day testing 9 he is my attempt at the alternative: Needs["RLink`"] InstallR[]; data = REvaluate["{ url <- \"http://www.massey.ac.nz/~pscowper/ts/cbe.dat\" CBE ...


4

Here is a worked example: We take as our data 10000 samples from a Normal Distribution with mean 1 and standard deviation 3: data = RandomVariate[NormalDistribution[1, 3], 10^4]; We then try to work backwards to see what the data says about the distribution - taking as an assumption that it came from a Normal Distribution, we see which paramaters are ...


4

This appears to work in the 9.0.1 release: DateListPlot[gasPrices]


4

My interest piqued, I went back to my original code, from 2002 (ok, not quite 15 years). Simon's comment was correct. The problem in the modified version of the code (not mine) was that it changed the obsolete command AppendRows to Join instead of Join[##,2]. This version produces the expected format result in version 9, and does so in a fraction of a ...


4

With[{data = #}, MapThread[{#1, #2} &, {(#1 + DateList[#2]*{0, 0, 0, 1, 1, 1}) & @@@ Tuples[{data[[2 ;;, 1]], Rest[data[[1]]]}], Flatten[data[[2 ;;, 2 ;;]]]}]] &[rawdata] All those excess characters... Here's the Twitterized version: Transpose@{(#1 + DateList[#2]*{0, 0, 0, 1, 1, 1}) & @@@\ Tuples@{#[[2 ;;, 1]], #[[1, 2 ...


4

Here is an approach using rawdata in the question: ans = Module[{time, dates, dt, values}, time = ToExpression /@ (StringSplit[#, ":"] & /@ Rest@First@rawdata); dates = rawdata[[2 ;;, 1]]; values = rawdata[[2 ;;, 2 ;;]]; dt = Map[Function[x, Join[x[[1 ;; 3]], #, {0}] & /@ time], dates]; Flatten[MapThread[{#1, #2} &, {dt, ...


4

It is not TimeSeriesWindow that is making your code slow. What is slowing down your function is the conversion of the data received from FinancialData from a List to a TimeSeries object. If this conversion is done before TimeSeriesWindow is applied, e.g. by tsData=TimeSeries[data] Than TimeSeriesWindow[tsData,{{2013,8,22},{2013,8,26}}] is even faster ...


3

To put Leonids comment into an answer: Please see the blog-post of Mike Honeychurch at ibnhconsulting.blogspot.ru. This should help you.


3

Yes, there is a way. We will illustrate this using the following example. data = Table[{3 + i + RandomReal[{-3, 7}], i + RandomReal[{-2, 5}]}, {i, 1, 20}]; model = LinearModelFit[data, x, x] (* ANOVA table *) model["ANOVATable"] $\begin{array}{l|lllll} \text{} & \text{DF} & \text{SS} & \text{MS} & \text{F-Statistic} & ...


3

You can do this: bas = FinancialData["MSFT", {DatePlus[Date[], -365], Date[]}, "Value"] (* Here bas is defined as the time-series of prices for Microsoft (ticker: MSFT) *) Now you can compute the log-returns: Differences[Log[bas]]


3

There are a couple issues here worth touching on, I think. Firstly, you are making things overly complicated for your self. Mathematica has a built in function for generating data from a random process, called RandomFunction. You can easily use it and a time series model to generate data, like so: armodel = ARProcess[{0.5469865826154379`, ...


3

I think your modeldata generation should be y[[t]] = whitenoise + (a1*y[[t-1]]) + (a2*y[[t-2]]) + (a3*y[[t-3]]) + (a4*y[[t-4]]) since for an AR(4) model, the current value is a linear combination of four previous values plus the current value of the noise. Also, after looking at the plot of processdata, I observed strong trend and seasonality, so you ...


3

Does the following direct implementation of your expressions work for you? ClearAll[data, dif, dbar, var, variogram]; dif[list_] := Table[(list[[k + 1 ;;]] - list[[;; -(k+1)]]), {k, Length[list] - 1}] var[list_] := Variance /@ (Most@(dif[list])); variogram[list_] := (#/First@#) &@var[list]; usage data = Accumulate[RandomReal[{-1, 1}, ...


3

More an extended comment than a complete answer or "how to". Vialiy's comment and the few pages Stephen Wolfram writes on the subject in A New Kind of Science, certainly provide a good place to start. Adding some basic understanding of types of participants might improve your chances of analyzing this problem with CellularAutomaton. Within any ...



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