prediction vs labels

[TayyabaZainab0807]

Upon using python train.py lstnet solar_energy --context_points 168 --target_points 24 --run_name spatiotemporal_al_solar --batch_size 25:

test/loss 0.11 test/mae. 1.82 test/mape 19922692 test/mse 12.67 test/norm_mae 0.18 test/norm_mse. 0.11 test/smape. 1.417

question1: why the mape is so big? are these results correct? I wanted to get the predictions and manually compare them with the labels using the following code:

        forecaster.eval()
        test_dataloader = data_module.test_dataloader()
        for batch in test_dataloader:
            # Extract input features from the batch
            xc, yc, xt, yt = batch  # Assuming these are the keys in your dataset
            # Make predictions using the forecaster
            yt_pred = forecaster.predict(xc, yc, xt,yt)
            print(yt_pred)
            print(yt)
            break

This is the output: `tensor([[[ 0.2024, 0.0527, 0.0133, ..., -0.0635, 0.0884, -0.0149], [ 0.2010, 0.0525, 0.0131, ..., -0.0636, 0.0879, -0.0150], [ 0.2041, 0.0523, 0.0131, ..., -0.0637, 0.0892, -0.0148], ..., [ 0.7595, 0.0227, 0.0320, ..., -0.0812, 0.2956, 0.0085], [ 0.8442, 0.0236, 0.0376, ..., -0.0806, 0.3280, 0.0181], [ 0.8337, 0.0235, 0.0369, ..., -0.0809, 0.3239, 0.0163]],

    [[ 0.2031,  0.0525,  0.0132,  ..., -0.0635,  0.0887, -0.0148],
     [ 0.2030,  0.0523,  0.0131,  ..., -0.0636,  0.0887, -0.0148],
     [ 0.2059,  0.0520,  0.0130,  ..., -0.0638,  0.0898, -0.0146],
     ...,
     [ 0.8137,  0.0230,  0.0353,  ..., -0.0811,  0.3162,  0.0142],
     [ 0.8793,  0.0247,  0.0404,  ..., -0.0798,  0.3414,  0.0224],
     [ 0.8796,  0.0252,  0.0408,  ..., -0.0797,  0.3416,  0.0229]],

    [[ 0.2051,  0.0523,  0.0131,  ..., -0.0635,  0.0896, -0.0146],
     [ 0.2048,  0.0520,  0.0129,  ..., -0.0637,  0.0893, -0.0147],
     [ 0.2073,  0.0514,  0.0128,  ..., -0.0639,  0.0901, -0.0150],
     ...,
     [ 0.8487,  0.0240,  0.0380,  ..., -0.0805,  0.3296,  0.0183],
     [ 0.9288,  0.0267,  0.0446,  ..., -0.0783,  0.3606,  0.0295],
     [ 0.9326,  0.0263,  0.0444,  ..., -0.0791,  0.3620,  0.0290]],

    ...,

    [[ 0.4881,  0.0286,  0.0199,  ..., -0.0763,  0.1952, -0.0072],
     [ 0.5514,  0.0270,  0.0233,  ..., -0.0777,  0.2192, -0.0024],
     [ 0.6315,  0.0262,  0.0280,  ..., -0.0786,  0.2497,  0.0051],
     ...,
     [ 0.8561,  0.0438,  0.0564,  ..., -0.0773,  0.3446,  0.0408],
     [ 0.8403,  0.0452,  0.0564,  ..., -0.0763,  0.3386,  0.0404],
     [ 0.8614,  0.0461,  0.0588,  ..., -0.0754,  0.3468,  0.0431]],

    [[ 0.5504,  0.0270,  0.0233,  ..., -0.0778,  0.2188, -0.0025],
     [ 0.6529,  0.0259,  0.0291,  ..., -0.0788,  0.2578,  0.0069],
     [ 0.7530,  0.0256,  0.0349,  ..., -0.0795,  0.2957,  0.0162],
     ...,
     [ 0.8595,  0.0458,  0.0580,  ..., -0.0759,  0.3459,  0.0428],
     [ 0.8386,  0.0458,  0.0571,  ..., -0.0757,  0.3378,  0.0406],
     [ 0.8749,  0.0465,  0.0601,  ..., -0.0752,  0.3520,  0.0446]],

    [[ 0.6496,  0.0259,  0.0290,  ..., -0.0789,  0.2564,  0.0067],
     [ 0.7954,  0.0254,  0.0371,  ..., -0.0797,  0.3117,  0.0198],
     [ 0.8922,  0.0258,  0.0428,  ..., -0.0797,  0.3481,  0.0284],
     ...,
     [ 0.8682,  0.0466,  0.0594,  ..., -0.0751,  0.3492,  0.0442],
     [ 0.8442,  0.0460,  0.0578,  ..., -0.0757,  0.3398,  0.0411],
     [ 0.8905,  0.0470,  0.0614,  ..., -0.0748,  0.3581,  0.0463]]])

tensor([[[-0.8025, -0.7136, -0.6984, ..., -0.7011, -0.7864, -0.6945], [-0.8025, -0.7136, -0.6984, ..., -0.7011, -0.7864, -0.6945], [-0.8025, -0.7136, -0.6984, ..., -0.7011, -0.7864, -0.6945], ..., [ 0.9684, 0.1093, 0.3574, ..., 0.3511, 1.0403, 0.2583], [ 1.0116, 0.2213, 0.5154, ..., 0.5210, 1.1411, 0.4755], [ 1.0467, 0.4340, 0.6848, ..., 0.6800, 1.2230, 0.7388]],

    [[-0.8025, -0.7136, -0.6984,  ..., -0.7011, -0.7864, -0.6945],
     [-0.8025, -0.7136, -0.6984,  ..., -0.7011, -0.7864, -0.6945],
     [-0.8025, -0.7136, -0.6984,  ..., -0.7011, -0.7864, -0.6945],
     ...,
     [ 1.0116,  0.2213,  0.5154,  ...,  0.5210,  1.1411,  0.4755],
     [ 1.0467,  0.4340,  0.6848,  ...,  0.6800,  1.2230,  0.7388],
     [ 1.0521,  0.6411,  0.8429,  ...,  0.7512,  1.2734,  0.9018]],

    [[-0.8025, -0.7136, -0.6984,  ..., -0.7011, -0.7864, -0.6945],
     [-0.8025, -0.7136, -0.6984,  ..., -0.7011, -0.7864, -0.6945],
     [-0.8025, -0.7136, -0.6984,  ..., -0.7011, -0.7864, -0.6945],
     ...,
     [ 1.0467,  0.4340,  0.6848,  ...,  0.6800,  1.2230,  0.7388],
     [ 1.0521,  0.6411,  0.8429,  ...,  0.7512,  1.2734,  0.9018],
     [ 1.0251,  0.7811,  0.9897,  ...,  0.8718,  1.3616,  1.1442]],

    ...,

    [[ 1.0116,  0.2213,  0.5154,  ...,  0.5210,  1.1411,  0.4755],
     [ 1.0467,  0.4340,  0.6848,  ...,  0.6800,  1.2230,  0.7388],
     [ 1.0521,  0.6411,  0.8429,  ...,  0.7512,  1.2734,  0.9018],
     ...,
     [ 1.2059,  1.8111,  2.0511,  ...,  1.9898,  1.2419,  2.0760],
     [ 0.7632,  1.6935,  2.0793,  ...,  2.0446,  1.2923,  2.1972],
     [ 1.0710,  1.5648,  2.1019,  ...,  2.0720,  1.3427,  2.1429]],

    [[ 1.0467,  0.4340,  0.6848,  ...,  0.6800,  1.2230,  0.7388],
     [ 1.0521,  0.6411,  0.8429,  ...,  0.7512,  1.2734,  0.9018],
     [ 1.0251,  0.7811,  0.9897,  ...,  0.8718,  1.3616,  1.1442],
     ...,
     [ 0.7632,  1.6935,  2.0793,  ...,  2.0446,  1.2923,  2.1972],
     [ 1.0710,  1.5648,  2.1019,  ...,  2.0720,  1.3427,  2.1429],
     [ 0.9198,  1.2289,  2.1414,  ...,  2.0884,  1.3112,  2.1763]],

    [[ 1.0521,  0.6411,  0.8429,  ...,  0.7512,  1.2734,  0.9018],
     [ 1.0251,  0.7811,  0.9897,  ...,  0.8718,  1.3616,  1.1442],
     [ 0.5824,  0.6019,  1.1195,  ...,  1.0417,  1.4183,  1.1358],
     ...,
     [ 1.0710,  1.5648,  2.1019,  ...,  2.0720,  1.3427,  2.1429],
     [ 0.9198,  1.2289,  2.1414,  ...,  2.0884,  1.3112,  2.1763],
     [ 1.1438,  1.1449,  2.1470,  ...,  2.0830,  1.2797,  2.2348]]])`
     
     

question2: In AL-solar, there are no negatives but the labels have some negative values? are the some scaling going on in here? question3: even then my predictions are not at all near to the labels (I am using the provided prediction method), any idea why?