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There are several methods to make forecasts of equidistant time series (e.g. Holt-Winters, ARIMA, ...). However I am currently working on the following irregular spaced data set, which has a varying amount of data points per year and no regular time intervals between those points:

Plot: plot Sample Data:

structure(list(date = structure(c(664239600, 665449200, 666658800, 
670888800, 672184800, 673394400, 674517600, 675727200, 676936800, 
678146400, 679356000, 680565600, 682984800, 684194400, 685404000, 
686613600, 687823200, 689036400, 690246000, 691455600, 692665200, 
695084400, 696294000, 697503600, 698713200, 699922800, 701132400, 
703548000, 705967200, 707176800, 708472800, 709682400, 710805600, 
712015200, 713224800, 714434400, 715644000, 716853600, 718063200, 
719272800, 720486000, 721695600, 722905200, 724114800, 726534000, 
727743600, 728953200, 730162800, 732668400, 733788000, 734911200, 
737416800, 739144800, 741650400, 744069600, 746575200, 751413600, 
756169200, 761612400, 766533600, 771285600, 776124000, 780962400, 
785804400, 790642800, 795481200, 800316000, 805154400, 808869600, 
813708000, 818463600, 823302000, 828741600, 833580000, 838418400, 
843256800, 848098800, 853542000, 858380400, 863215200, 868053600, 
872892000, 875311200, 880153200, 884991600, 892291920, 897122048, 
901956780, 907055160, 912501900, 917083860, 919500720, 924354660, 
929104882, 934013100, 938851554, 948540840, 958809480, 963647580
), class = c("POSIXct", "POSIXt"), tzone = ""), y = c(3.36153, 
-0.48246, 5.21884, 18.74093, 37.91793, 28.54938, 33.61709, 63.06235, 
68.65387, 77.23859, 87.11039, 84.03281, 93.62154, 99.91251, 100.50264, 
93.77179, 84.5999, 67.36365, 41.30507, 18.19424, 0.958, -15.81843, 
-14.5947, 5.63223, 6.98581, 4.49837, 12.14337, 26.38595, 38.18156, 
39.49169, 45.91298, 64.2627, 65.20289, 95.34555, 98.09912, 102.53325, 
101.76982, 95.17178, 93.00834, 81.43244, 59.84896, 44.55941, 
22.71526, 8.64943, 12.36012, -3.73631, -1.29231, -1.24887, 27.38948, 
33.22064, 28.50297, 39.53514, 52.27092, 64.83294, 79.8159, 107.36236, 
69.52707, 12.95026, 13.36662, 27.65264, 61.13918, 82.24249, 85.89012, 
13.9803, -11.97099, 8.03575, 55.61148, 93.62154, 107.10067, 88.11689, 
18.06141, -32.83151, 18.01798, 60.92196, 100.39437, 112.40503, 
54.1048, 2.59809, 31.10314, 56.46477, 58.4749, 124.68055, 100.5016, 
43.5316, -7.5386, 35.20915, 37.08925, 83.0716, 83.22325, 29.5081, 
-32.7452, -50.63345, 29.00605, 58.2997, 85.3864, 110.4178, -38.66195, 
16.16515, 71.64925)), .Names = c("date", "y"), row.names = c(NA, 
-99L), class = "data.frame")

My first thought was aggregating the data by calculating monthly averages. However this will lead to many months with missing values and secondly accuracy will be lost if multiple values within a month are replaced by a mean aggregat. To solve the first problem one could propose to calculate quarterly aggregates. But in this case the data sample would get relatively small.

So my question is how your approach would look like to make a forecast of the next data point for the given data set (if possible with R). Are there any best practices to handle the irregular spaced time series?

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  • $\begingroup$ Data imputation can be done using Mean imputation, Regression imputation, etc. I am using one such implementation for my problem. I am also looking into non-imputation methods. If i find one, i will let it be known. $\endgroup$ – Himadri Das Apr 14 at 4:42
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State space models support the missing data very well. take a look at section 6.4 "Missing Data Modifications" in Time Series Analysis and Its Applications With R Examples, 3rd ed., by Shumway and Stoffer. They have examples in http://www.stat.pitt.edu/stoffer/tsa3/

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Since the interval between two observations is not constant, we are left with two options

  1. Treat the observations as regular time series with missing data. In this case, we need to impute missing values. There is a list of imputation techniques discussed here : https://towardsdatascience.com/6-different-ways-to-compensate-for-missing-values-data-imputation-with-examples-6022d9ca0779. Then use any regular time series forecasting method like ARIMA, Exponential Smoothing, LSTM, etc.

  2. Treat the observations as irregular as they are and use techniques discussed here : https://www.sciencedirect.com/science/article/pii/0169207086990047, https://www.sciencedirect.com/science/article/pii/S2352340920306739

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    $\begingroup$ Welcome to the site, @HimadriDas. Thank you for adding sources & improving your answer. I think this is a viable contribution to this thread now. Instead of reposting the answer that had been deleted, it would have been better to flag your answer & ask the moderators to undelete it, though. Since you're new here, you may want to take our tour, which has information for new users. $\endgroup$ – gung - Reinstate Monica Apr 16 at 13:19

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