AI LAYER
TimeSeries

TimeSeries / GenerativeForecast / 0.2.6

GenerativeForecast

10 Credit Royalty
Forecast the next N steps in your timeseries with Machine Learning.
forecast incremental training multivariate pytorch regression timeseries
Language
Python 3.x
Metrics
API Calls - 5,227 Avg call duration - N/A
Permissions
The Algorithm Platform License is the set of terms that are stated in the Software License section of the Algorithmia Application Developer and API License Agreement. It is intended to allow users to reserve as many rights as possible without limiting Algorithmia's ability to run it as a service. Learn More
This is necessary for algorithms that rely on external services, however it also implies that this algorithm is able to send your input data outside of the Algorithmia platform.
This allows an algorithm to compose sophisticated functionality using other algorithms as building blocks, however it also carries the potential of incurring additional royalty and usage costs from any algorithm that it calls.

Run an Example

Input
Output
{
  "envelope": [
    {
      "first_deviation": {
        "lower_bound": [
          -0.24974047880307135,
          -0.1626398002057803,
          -0.09221836034613362,
          -0.04060741728650548,
          0.010438189471517973,
          0.05566651393744462,
          0.10995791852446236,
          0.15971764906377603,
          0.20723618534781688,
          0.25230980070429415,
          0.3069362930982914,
          0.358120617806828,
          0.4057062141141868,
          0.4517036959079879,
          0.49394331833939287
        ],
        "upper_bound": [
          -0.20666794353078224,
          -0.13045106438445583,
          -0.06080309787094226,
          0.0013499692263335382,
          0.05289001877062593,
          0.0984071535888005,
          0.14580966532258355,
          0.19639043057402392,
          0.2452731770091865,
          0.2968010887305203,
          0.3457551324976733,
          0.3952010775209308,
          0.4438480248864061,
          0.489102093494565,
          0.5350962348941626
        ]
      },
      "mean": [
        -0.2282042111669268,
        -0.14654543229511807,
        -0.07651072910853794,
        -0.019628724030085972,
        0.03166410412107195,
        0.07703683376312256,
        0.12788379192352295,
        0.17805403981889997,
        0.22625468117850167,
        0.2745554447174072,
        0.32634571279798236,
        0.3766608476638794,
        0.4247771195002965,
        0.4704028947012765,
        0.5145197766167777
      ],
      "second_deviation": {
        "lower_bound": [
          -0.2712767464392159,
          -0.17873416811644255,
          -0.10792599158372931,
          -0.06158611054292499,
          -0.010787725178036008,
          0.03429619411176668,
          0.09203204512540178,
          0.14138125830865209,
          0.18821768951713208,
          0.2300641566911811,
          0.2875268733986005,
          0.3395803879497767,
          0.38663530872807705,
          0.4330044971146993,
          0.473366860062008
        ],
        "upper_bound": [
          -0.1851316758946377,
          -0.1143566964737936,
          -0.04509546663334658,
          0.02232866248275305,
          0.07411593342017991,
          0.11977747341447843,
          0.16373553872164412,
          0.21472682132914783,
          0.26429167283987126,
          0.3190467327436334,
          0.3651645521973642,
          0.41374130737798215,
          0.46291893027251585,
          0.5078012922878536,
          0.5556726931715474
        ]
      },
      "standard_deviation": [
        0.021536267636144545,
        0.016094367910662235,
        0.015707631237595682,
        0.02097869325641951,
        0.02122591464955398,
        0.02137031982567794,
        0.01792587339906059,
        0.01833639075512394,
        0.019018495830684788,
        0.022245644013113065,
        0.01940941969969093,
        0.018540229857051373,
        0.019070905386109695,
        0.01869919879328859,
        0.02057645827738487
      ],
      "variable": "0"
    }
  ],
  "saved_graph_path": "data://.algo/timeseries/generativeforecast/temp/my_sinegraph.png"
}

Install and Use

Install

Install the Algorithmia CLI client by running: 

curl -sSLf https://algorithmia.com/install.sh | sh

Then authenticate by running:

$ algo auth # When prompted for api endpoint,
        hit enter
        # When prompted for API key, enter your key:
        YOUR_API_KEY
CLI Install Docs

Use



  algo run TimeSeries/GenerativeForecast/0.2.6 -d '{  
   "forecast_size":15,
   "iterations":35,
   "io_noise":0.05,
   "mode":"forecast",
   "graph_save_path":"data://.algo/timeseries/generativeforecast/temp/my_sinegraph.png",
   "checkpoint_input_path":"data://timeseries/generativeforecasting/sinewave_v1.0_t0.zip"
}' --timeout 300
CLI Docs