Sida la og yahay ummad kasta oo af, dhaqan iyo hidde wadaagta waxay leedahay murti iyo suugaan u gaar ah oo ay ku hirtaan. Hal-abuurayaasha dejiya suugaantaasna waxaa la og yahay iney door weyn ka cayaaraan isuduwidda, kicinta iyo horumarinta ummaddaas. Sayid Maxamed wuxuu ka mid yahay abwaannada waaweyn ee taariikhda Soomaalida meel ka galay. Taasna waxaa u aqoonsan dadkiisa iyo dadyowga kale markay u sugnaatay inuu ahaa geesi waddani ah, gabyaa aftahan ah, buuni cilmi leh, siyaasi xirribbo badan.

Intaaba waxaa marag u ah gabayadiisii jaad walba leh, waxaana garan kara qofkii murtidiisa dhuuxa marka laga eego xagga suugaanta, siyaasadda, wadaninnimada iyo aftahannimada ku jirta. Diiwaankan waxaa ku qoran gabay oo Sayid Maxamed tiriyey intii u dhexeysay tii ilaa kii, iyo 16 gabay oo gabayada Sayidka xiriir la leh.

Guud ahaan waxay gabayadaasi muujinayaan dhaqankii Soomaalida iyo heerkii suugaantu waqtigaas joogtay.

Marka waxbarasho guud mooyee, waxay gabayadu albaab weyn u furayaan qoraalka suugaanta af-Soomaaligu hodonka ku yahay. Waxayna dhaxal u noqon doonaan dadka danbe ee wax qori doona. Kuwaas oo buuggan ka heli doona tusaalooyin badan oo waxtar u leh. Odayaashii tan iyo muddo dheer u guntaday inay uruuriyaan ka dibna qoraan buuggan, waxaa sharaf iyo abaalmarinba ugu filan inay wariyeen wixii mahadho Sayidka laga hayey, anaguna waxay naga mutaysteen mahad iyo ammaan.

Diiwaankan Sayid Maxamed wuxuu ka kooban yahay gabay oo ka mid ah gabayadii Sayidka. Sheekh Jaamac wuxuu kaloo ka mid yahay ragga gabayada, taariikhda Sayidka iyo Dhaqdhaqaaqii Daraawiishta dhab u yaqaan kuwa ugu fiican.

Gabayadu waxay muujinayaan taariikhdii weyneyd ee Sayidka iyo halganki Daraawiishta. Waxay kaloo tusayaan heerka sare ee Sayidku murtida iyo suugaanta Soomaalida ka joogay iyo sida uu suugaanta ugu xeel dheeraa. Diiwaankan waxaa Guddiga Af Soomaaliga u soo gudbiyey Wasaaradda Hiddaha iyo Tacliinta Sare iyo Akadeemiyada Dhaqanka si ay isha u mariyaan, waxay u la ekaataba uga soo yiraahdaan.

Dabadeed haddii guddigu eegay, aad buu ugu bogey gabayada iyo habka wanaagsan ee loo uruurshay labadaba. Waxaa ila gudboon inaan u mahad naqo dadkii buuggan qoristiisa, garaaciddiisii iyo isuduwiddiisa igu gargaaray.

Dadkaasna abaalkooda Eebbe weyne ayaa gudi kara. Xusuus gaar ah waxaa iga mudan raggii Daraawiisheed ee gabayadan laga soo guuriyey oo badidoodii ciidda ka hoos mareen. Afka Soomaalidu waa af ballaaran, erayadiisuna badan yihiin, dhan loo eegaba hadal iyo hawraar, maanso iyo maahmaah, gabay iyo geeraar, masafo iyo madaddaalo, hees iyo heello, sheeko iyo shararraaxin, qorriin iyo qun-u-hadal, gees loo dayaba waa af hodon ah, oo murti iyo macaan ka buuxaan, qoto dheeri iyo qaayana Eebbe weyne u yeelay, dadka ku hadlaana ku kalsoon yihiin, dantooda iyo waxay doonayaanna ku sheegan karaan.

Eray walba oo erayadiisa ka mid ihi wuxuu leeyahay dhowr eray oo la mid ah ama la macno ah. Eray walba waxaa ka abuurma dhawr eray oo kale. Af Soomaaliga wuxuu ka mid yahay afafka looga hadlo Geeska Afrika, welina run ahaan looma oga afka uu la bah yahay. Xiriir dheerna wuxuu la leeyahay afka Gaallaha.

Inkastoo af Soomaaliga aan dhab loo baarin lama oga taariikhdii maansada la billaabay. Gaar ahaan gabayga, oday ma sheegin, wiilna aabbihii kama werin.

Waxaa hubaal ah hadaynu dhab ugu fiirsanno dhismaha guud ee hadalka ama maansada xeerka loo mariyo ama habka iyo hannaanka gabaygu u dhisan yahay iyo sida qof waliba u yaqaan waddada loo maro ama sida deelqaafka iyo laaxinka looga ilaaliyo waxaad garan kartaa inuu af Soomaaligu fil weyn yahay oo taariikh dheer soo maray. Murtidu waxay u kala baxdaa maanso iyo hees. Heesku wuxuu u kala baxaa laba qeybood: hees hawleed iyo hees cayaareed. Hees hawleed waa heeska hawshu lagu qabto, hawshaasu hawl xoolaad ha ahaato ama hawl farsamo ha ahaatee.

Hees cayaareedna waa heeskii sacab ama jaanta loo tumo ama durbaan loo garaaco oo looga jeedo farax, maaweello, madaddaalo iwm. Labadaas qaybood oo aanu soo sheegnay haddii la falanqeeyo mid waliba wuxuu noqonayaa bad aan xeeb lahayn iyo berri aan laga hoyan karin iyo biyo aan laga bixi karin. Toddobadaas qaybood ee maansadu u kala baxdo waxaa calanka u sida gabayga.

Siday isu xigaan bay u kala qaaya weyn yihiin. Soomaalidii hore ninka gabyaaga ah iyo gabayguba qaayo weyn iyo qiima badan bay la lahaayeen, dadka dhexdiisana xurmo iyo xarragay ku lahaayeen, xulquun iyo xoolana waa u oggolaayeen, maxaa wacay gabaygu wuxuu ka mid ahaa ciidanka iyo gaashaandhigga dadku ku tala galo.

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You can also list all of your time series. This can be used to change the names of the fields in the time series with respect to the original names in the dataset or to tell BigML that certain fields should be preferred. Example: 100 name optional String,default is dataset's name The name you want to give to the new time series.

The type of the field must be numerical. Non-numeric fields will be ignored, and if not present, the right-most valid field in the dataset will be used. The period needs to be set taking into account the time interval of your instances and the seasonal frequency. For example, for monthly data and annual seasonality, the period should be 12, for daily data and weekly seasonality, the period should be 7.

It can take values from 0 to 60. If the period is set to 1, there is no seasonality. If the period is 0, or not given, BigML will automatically learn the period in your data. The range of successive instances to build the time series. Multiplicative seasonality models are only available when the objective field has strictly positive values (greater than 0). Example: 2 tags optional Array of Strings A list of strings that help classify and index your time series.

If absent, the first datetime field in the dataset whose values are continuously either decreasing or increasing. If not given, the server will auto-detect the first sequential datetime field in the dataset.

All fields are optional: giving any two among start, end, and interval are enough for a full specification, since the remaining one can always be computed. If you give all three, end is ignored and recomputed using start and interval. After the initial pass through the input data, the value of end will be adjusted to coincide with the last non-missing objective value.

If the objective field has missing values at its tail, then this adjusted value will differ from the one specified or computed from start and interval. If you do not specify an objective field, BigML. Once a time series has been successfully created it will have the following properties. Each field's id has a list of objects with the following properties: The property forecast is a dictionary keyed by each field's id in the source. Each field's id has a list of objects with the following properties: In addition to the ETS models, BigML also provides simple forecast models for each field, to be used as references for the performance of the ETS models.

Due to their trivial nature, these are always computed regardless of what ETS parameters are selected in the input. Currently, we offer three simple model types: naive, mean, and drift. Naive: this model always forecasts the last value of the observed time series. For seasonal models, it repeats the last m values of the training series, where "m" is the given period length for the field. The parameters for this field are as follows: Mean: this model always forecasts the mean of the objective field.

For seasonal models, it is similar to the naive model since the model cycles the same sequence of values for forecasts, but instead of using the last set of m values, BigML computes the mean sequence of the naive values. The parameters for this field are as follows: Drift: Draws a straight line between the first and last values of the training series.

Forecasts are performed by extending that line. The parameters for this field are as follows: Creating a time series is a process that can take just a few seconds or a few days depending on the size of the dataset used as input and on the workload of BigML's systems.

The time series goes through a number of states until its fully completed. Through the status field in the time series you can determine when time series has been fully processed and ready to be used to create forecasts. Thus when retrieving a timeseries, it's possible to specify that only a subset of fields be retrieved, by using any combination of the following parameters in the query string (unrecognized parameters are ignored): Fields Filter Parameters Parameter TypeDescription fields optional Comma-separated list A comma-separated list of field IDs to retrieve.

To update a time series, you need to PUT an object containing the fields that you want to update to the time series' base URL. Once you delete a time series, it is permanently deleted.


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