Imaging Time Series

Main functions used to transform time series into TSImage tensors.

dsid = 'NATOPS'
X, y, splits = get_UCR_data(dsid, return_split=False)

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ToTSImage

def ToTSImage(
    enc:NoneType=None, dec:NoneType=None, split_idx:NoneType=None, order:NoneType=None
):

Delegates (__call__,decode,setup) to (encodes,decodes,setups) if split_idx matches

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TSImage

def TSImage(
    args:VAR_POSITIONAL, kwargs:VAR_KEYWORD
):

A Tensor which support subclass pickling, and maintains metadata when casting or after methods

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TSToPlot

def TSToPlot(
    size:Optional=224, dpi:int=100, lw:int=1, kwargs:VAR_KEYWORD
):

Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by creating a matplotlib plot.

out = TSToPlot()(TSTensor(X[:2]), split_idx=0)
print(out.shape)
out[0].show()

torch.Size([2, 3, 224, 224])

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TSToMat

def TSToMat(
    size:int=224, dpi:int=100, cmap:NoneType=None, kwargs:VAR_KEYWORD
):

Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by creating a matplotlib matrix. Input data must be normalized with a range(-1, 1)

out = TSToMat()(TSTensor(X[:2]), split_idx=0)
print(out.shape)
out[0].show()

torch.Size([2, 3, 224, 224])

out = TSToMat(cmap='spring')(TSTensor(X[:2]), split_idx=0)
print(out.shape)
out[0].show()

torch.Size([2, 3, 224, 224])

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TSToJRP

def TSToJRP(
    size:int=224, cmap:NoneType=None, kwargs:VAR_KEYWORD
):

Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Joint Recurrence Plot

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TSToRP

def TSToRP(
    size:int=224, cmap:NoneType=None, kwargs:VAR_KEYWORD
):

Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Recurrence Plot. It requires input to be previously normalized between -1 and 1

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TSToMTF

def TSToMTF(
    size:int=224, cmap:NoneType=None, n_bins:int=5, kwargs:VAR_KEYWORD
):

Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Markov Transition Field

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TSToGASF

def TSToGASF(
    size:int=224, cmap:NoneType=None, range:NoneType=None, kwargs:VAR_KEYWORD
):

Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Gramian Angular Summation Field. It requires either input to be previously normalized between -1 and 1 or set range to (-1, 1)

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TSToGADF

def TSToGADF(
    size:int=224, cmap:NoneType=None, range:NoneType=None, kwargs:VAR_KEYWORD
):

Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Gramian Angular Difference Field. It requires either input to be previously normalized between -1 and 1 or set range to (-1, 1)

out = TSToRP()(TSTensor(X[:2]), split_idx=0)
print(out.shape)
out[0].show()

torch.Size([2, 24, 224, 224])

o = TSTensor(X[0][1][None])
encoder = RecurrencePlot()
a = encoder.fit_transform(o.cpu().numpy())[0]
o = TSTensor(X[0])
encoder = RecurrencePlot()
b = encoder.fit_transform(o.cpu().numpy())[1]
test_eq(a,b) # channels can all be processed in parallel

test_eq(TSToRP()(TSTensor(X[0]), split_idx=False)[0], TSToRP()(TSTensor(X[0][0][None]), split_idx=False)[0])
test_eq(TSToRP()(TSTensor(X[0]), split_idx=False)[1], TSToRP()(TSTensor(X[0][1][None]), split_idx=False)[0])
test_eq(TSToRP()(TSTensor(X[0]), split_idx=False)[2], TSToRP()(TSTensor(X[0][2][None]), split_idx=False)[0])

dsid = 'NATOPS'
X, y, splits = get_UCR_data(dsid, return_split=False)
tfms = [None, Categorize()]
bts = [[TSNormalize(), TSToPlot(100)],
       [TSNormalize(), TSToMat(100)],
       [TSNormalize(), TSToGADF(100)],
       [TSNormalize(), TSToGASF(100)],
       [TSNormalize(), TSToMTF(100)],
       [TSNormalize(), TSToRP(100)]]
btns = ['Plot', 'Mat', 'GADF', 'GASF', 'MTF', 'RP']
dsets = TSDatasets(X, y, tfms=tfms, splits=splits)
for i, (bt, btn) in enumerate(zip(bts, btns)):
    dls = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=8, batch_tfms=bt)
    test_eq(dls.vars, 3 if i <2 else X.shape[1])
    test_eq(dls.vars, 3 if i <2 else X.shape[1])
    test_eq(dls.len, (100,100))
    xb, yb = dls.train.one_batch()
    print(i, btn, xb, xb.dtype, xb.min(), xb.max())
    xb[0].show()
    plt.show()

0 Plot TSImage(shape:torch.Size([8, 3, 100, 100])) torch.float32 0.054901961237192154 1.0

1 Mat TSImage(shape:torch.Size([8, 3, 100, 100])) torch.float32 0.019607843831181526 1.0

2 GADF TSImage(shape:torch.Size([8, 24, 100, 100])) torch.float32 2.980232238769531e-07 0.9999997019767761

3 GASF TSImage(shape:torch.Size([8, 24, 100, 100])) torch.float32 0.0 0.938302218914032

4 MTF TSImage(shape:torch.Size([8, 24, 100, 100])) torch.float32 0.0 1.0

5 RP TSImage(shape:torch.Size([8, 24, 100, 100])) torch.float32 0.0 0.8106333613395691

The simplest way to train a model using time series to image transforms is this:

dsid = 'NATOPS'
X, y, splits = get_UCR_data(dsid, return_split=False)
tfms = [None, Categorize()]
batch_tfms = [TSNormalize(), TSToGADF(224)]
dls = get_ts_dls(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)
learn = tsimage_learner(dls, xresnet34)
learn.fit_one_cycle(10)