Bayesian Temporal Tensor Factorization (or BTTF for short) is a type of Bayesian tensor decomposition that achieves state-of-the-art results on challenging the missing data imputation problem. In the following, we will discuss:

• What the BTTF is?

• How to implement BTTF mainly using Python Numpy with high efficiency?

• How to make imputations with real-world spatiotemporal datasets?

If you want to understand what is BTMF and its modeling tricks in detail, our paper is for you:

Xinyu Chen, Lijun Sun (2019). Bayesian temporal factorization for multidimensional time series prediction.

## Quick Run¶

This notebook is publicly available for any usage at our data imputation project. Please click transdim.

In [1]:
import numpy as np
from numpy.random import multivariate_normal as mvnrnd
from scipy.stats import wishart
from numpy.linalg import inv as inv


# Part 1: Matrix Computation Concepts¶

## 1) Kronecker product¶

• Definition:

Given two matrices $A\in\mathbb{R}^{m_1\times n_1}$ and $B\in\mathbb{R}^{m_2\times n_2}$, then, the Kronecker product between these two matrices is defined as

$$A\otimes B=\left[ \begin{array}{cccc} a_{11}B & a_{12}B & \cdots & a_{1m_2}B \\ a_{21}B & a_{22}B & \cdots & a_{2m_2}B \\ \vdots & \vdots & \ddots & \vdots \\ a_{m_11}B & a_{m_12}B & \cdots & a_{m_1m_2}B \\ \end{array} \right]$$

where the symbol $\otimes$ denotes Kronecker product, and the size of resulted $A\otimes B$ is $(m_1m_2)\times (n_1n_2)$ (i.e., $m_1\times m_2$ columns and $n_1\times n_2$ rows).

• Example:

If $A=\left[ \begin{array}{cc} 1 & 2 \\ 3 & 4 \\ \end{array} \right]$ and $B=\left[ \begin{array}{ccc} 5 & 6 & 7\\ 8 & 9 & 10 \\ \end{array} \right]$, then, we have

$$A\otimes B=\left[ \begin{array}{cc} 1\times \left[ \begin{array}{ccc} 5 & 6 & 7\\ 8 & 9 & 10\\ \end{array} \right] & 2\times \left[ \begin{array}{ccc} 5 & 6 & 7\\ 8 & 9 & 10\\ \end{array} \right] \\ 3\times \left[ \begin{array}{ccc} 5 & 6 & 7\\ 8 & 9 & 10\\ \end{array} \right] & 4\times \left[ \begin{array}{ccc} 5 & 6 & 7\\ 8 & 9 & 10\\ \end{array} \right] \\ \end{array} \right]$$$$=\left[ \begin{array}{cccccc} 5 & 6 & 7 & 10 & 12 & 14 \\ 8 & 9 & 10 & 16 & 18 & 20 \\ 15 & 18 & 21 & 20 & 24 & 28 \\ 24 & 27 & 30 & 32 & 36 & 40 \\ \end{array} \right]\in\mathbb{R}^{4\times 6}.$$

## 2) Khatri-Rao product (kr_prod)¶

• Definition:

Given two matrices $A=\left( \boldsymbol{a}_1,\boldsymbol{a}_2,...,\boldsymbol{a}_r \right)\in\mathbb{R}^{m\times r}$ and $B=\left( \boldsymbol{b}_1,\boldsymbol{b}_2,...,\boldsymbol{b}_r \right)\in\mathbb{R}^{n\times r}$ with same number of columns, then, the Khatri-Rao product (or column-wise Kronecker product) between $A$ and $B$ is given as follows,

$$A\odot B=\left( \boldsymbol{a}_1\otimes \boldsymbol{b}_1,\boldsymbol{a}_2\otimes \boldsymbol{b}_2,...,\boldsymbol{a}_r\otimes \boldsymbol{b}_r \right)\in\mathbb{R}^{(mn)\times r},$$

where the symbol $\odot$ denotes Khatri-Rao product, and $\otimes$ denotes Kronecker product.

• Example:

If $A=\left[ \begin{array}{cc} 1 & 2 \\ 3 & 4 \\ \end{array} \right]=\left( \boldsymbol{a}_1,\boldsymbol{a}_2 \right)$ and $B=\left[ \begin{array}{cc} 5 & 6 \\ 7 & 8 \\ 9 & 10 \\ \end{array} \right]=\left( \boldsymbol{b}_1,\boldsymbol{b}_2 \right)$, then, we have

$$A\odot B=\left( \boldsymbol{a}_1\otimes \boldsymbol{b}_1,\boldsymbol{a}_2\otimes \boldsymbol{b}_2 \right)$$$$=\left[ \begin{array}{cc} \left[ \begin{array}{c} 1 \\ 3 \\ \end{array} \right]\otimes \left[ \begin{array}{c} 5 \\ 7 \\ 9 \\ \end{array} \right] & \left[ \begin{array}{c} 2 \\ 4 \\ \end{array} \right]\otimes \left[ \begin{array}{c} 6 \\ 8 \\ 10 \\ \end{array} \right] \\ \end{array} \right]$$$$=\left[ \begin{array}{cc} 5 & 12 \\ 7 & 16 \\ 9 & 20 \\ 15 & 24 \\ 21 & 32 \\ 27 & 40 \\ \end{array} \right]\in\mathbb{R}^{6\times 2}.$$
In [2]:
def kr_prod(a, b):
return np.einsum('ir, jr -> ijr', a, b).reshape(a.shape[0] * b.shape[0], -1)

In [3]:
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8], [9, 10]])
print(kr_prod(A, B))

[[ 5 12]
[ 7 16]
[ 9 20]
[15 24]
[21 32]
[27 40]]


## 3) Computing Covariance Matrix (cov_mat)¶

For any matrix $X\in\mathbb{R}^{m\times n}$, cov_mat can return a $n\times n$ covariance matrix for special use in the following.

In [4]:
def cov_mat(mat):
dim1, dim2 = mat.shape
new_mat = np.zeros((dim2, dim2))
mat_bar = np.mean(mat, axis = 0)
for i in range(dim1):
new_mat += np.einsum('i, j -> ij', mat[i, :] - mat_bar, mat[i, :] - mat_bar)
return new_mat


## 4) CP decomposition (cp_combine)¶

• Definition:

The CP decomposition factorizes a tensor into a sum of outer products of vectors. For example, for a third-order tensor $\mathcal{Y}\in\mathbb{R}^{m\times n\times f}$, the CP decomposition can be written as

$$\hat{\mathcal{Y}}=\sum_{s=1}^{r}\boldsymbol{u}_{s}\circ\boldsymbol{v}_{s}\circ\boldsymbol{x}_{s},$$

or element-wise,

$$\hat{y}_{ijt}=\sum_{s=1}^{r}u_{is}v_{js}x_{ts},\forall (i,j,t),$$

where vectors $\boldsymbol{u}_{s}\in\mathbb{R}^{m},\boldsymbol{v}_{s}\in\mathbb{R}^{n},\boldsymbol{x}_{s}\in\mathbb{R}^{f}$ are columns of factor matrices $U\in\mathbb{R}^{m\times r},V\in\mathbb{R}^{n\times r},X\in\mathbb{R}^{f\times r}$, respectively. The symbol $\circ$ denotes vector outer product.

• Example:

Given matrices $U=\left[ \begin{array}{cc} 1 & 2 \\ 3 & 4 \\ \end{array} \right]\in\mathbb{R}^{2\times 2}$, $V=\left[ \begin{array}{cc} 1 & 2 \\ 3 & 4 \\ 5 & 6 \\ \end{array} \right]\in\mathbb{R}^{3\times 2}$ and $X=\left[ \begin{array}{cc} 1 & 5 \\ 2 & 6 \\ 3 & 7 \\ 4 & 8 \\ \end{array} \right]\in\mathbb{R}^{4\times 2}$, then if $\hat{\mathcal{Y}}=\sum_{s=1}^{r}\boldsymbol{u}_{s}\circ\boldsymbol{v}_{s}\circ\boldsymbol{x}_{s}$, then, we have

$$\hat{Y}_1=\hat{\mathcal{Y}}(:,:,1)=\left[ \begin{array}{ccc} 31 & 42 & 65 \\ 63 & 86 & 135 \\ \end{array} \right],$$$$\hat{Y}_2=\hat{\mathcal{Y}}(:,:,2)=\left[ \begin{array}{ccc} 38 & 52 & 82 \\ 78 & 108 & 174 \\ \end{array} \right],$$$$\hat{Y}_3=\hat{\mathcal{Y}}(:,:,3)=\left[ \begin{array}{ccc} 45 & 62 & 99 \\ 93 & 130 & 213 \\ \end{array} \right],$$$$\hat{Y}_4=\hat{\mathcal{Y}}(:,:,4)=\left[ \begin{array}{ccc} 52 & 72 & 116 \\ 108 & 152 & 252 \\ \end{array} \right].$$
In [5]:
def cp_combine(U, V, X):
return np.einsum('is, js, ts -> ijt', U, V, X)

In [6]:
U = np.array([[1, 2], [3, 4]])
V = np.array([[1, 3], [2, 4], [5, 6]])
X = np.array([[1, 5], [2, 6], [3, 7], [4, 8]])
print(cp_combine(U, V, X))
print()
print('tensor size:')
print(cp_combine(U, V, X).shape)

[[[ 31  38  45  52]
[ 42  52  62  72]
[ 65  82  99 116]]

[[ 63  78  93 108]
[ 86 108 130 152]
[135 174 213 252]]]

tensor size:
(2, 3, 4)


## 5) Tensor Unfolding (ten2mat)¶

Using numpy reshape to perform 3rd rank tensor unfold operation. [link]

In [7]:
import numpy as np
def ten2mat(tensor, mode):
return np.reshape(np.moveaxis(tensor, mode, 0), (tensor.shape[mode], -1), order = 'F')

In [8]:
X = np.array([[[1, 2, 3, 4], [3, 4, 5, 6]],
[[5, 6, 7, 8], [7, 8, 9, 10]],
[[9, 10, 11, 12], [11, 12, 13, 14]]])
print('tensor size:')
print(X.shape)
print('original tensor:')
print(X)
print()
print('(1) mode-1 tensor unfolding:')
print(ten2mat(X, 0))
print()
print('(2) mode-2 tensor unfolding:')
print(ten2mat(X, 1))
print()
print('(3) mode-3 tensor unfolding:')
print(ten2mat(X, 2))

tensor size:
(3, 2, 4)
original tensor:
[[[ 1  2  3  4]
[ 3  4  5  6]]

[[ 5  6  7  8]
[ 7  8  9 10]]

[[ 9 10 11 12]
[11 12 13 14]]]

(1) mode-1 tensor unfolding:
[[ 1  3  2  4  3  5  4  6]
[ 5  7  6  8  7  9  8 10]
[ 9 11 10 12 11 13 12 14]]

(2) mode-2 tensor unfolding:
[[ 1  5  9  2  6 10  3  7 11  4  8 12]
[ 3  7 11  4  8 12  5  9 13  6 10 14]]

(3) mode-3 tensor unfolding:
[[ 1  5  9  3  7 11]
[ 2  6 10  4  8 12]
[ 3  7 11  5  9 13]
[ 4  8 12  6 10 14]]


# Part 2: Bayesian Temporal Tensor Factorization (BTTF)¶

In [9]:
def BTTF(dense_tensor, sparse_tensor, init, rank, time_lags, maxiter1, maxiter2):
"""Bayesian Temporal Tensor Factorization, BTTF."""
U = init["U"]
V = init["V"]
X = init["X"]
theta = init["theta"]

d = theta.shape[0]
dim1, dim2, dim3 = sparse_tensor.shape
dim = np.array([dim1, dim2, dim3])
pos = np.where((dense_tensor != 0) & (sparse_tensor == 0))
position = np.where(sparse_tensor != 0)
binary_tensor = np.zeros((dim1, dim2, dim3))
binary_tensor[position] = 1

beta0 = 1
nu0 = rank
mu0 = np.zeros((rank))
W0 = np.eye(rank)
tau = 1
alpha = 1e-6
beta = 1e-6

mat_hat = np.zeros((dim1, dim2, dim3 + 1))
U_plus = np.zeros((dim1, rank))
V_plus = np.zeros((dim2, rank))
X_plus = np.zeros((dim3, rank))
X_new = np.zeros((dim3 + 1, rank))
X_new_plus = np.zeros((dim3 + 1, rank))
theta_plus = np.zeros((d, rank))
tensor_hat_plus = np.zeros((dim1, dim2, dim3 + 1))

for iters in range(maxiter1):
for order in range(2):
if order == 0:
mat = U.copy()
elif order == 1:
mat = V.copy()
mat_bar = np.mean(mat, axis = 0)
var_mu_hyper = (dim[order] * mat_bar + beta0 * mu0)/(dim[order] + beta0)
var_W_hyper = inv(inv(W0) + cov_mat(mat) + dim[order] * beta0/(dim[order] + beta0)
* np.outer(mat_bar - mu0, mat_bar - mu0))
var_W_hyper = (var_W_hyper + var_W_hyper.T)/2
var_Lambda_hyper = wishart(df = dim[order] + nu0, scale = var_W_hyper, seed = None).rvs()
var_mu_hyper = mvnrnd(var_mu_hyper, inv((dim[order] + beta0) * var_Lambda_hyper))

if order == 0:
var1 = kr_prod(X, V).T
elif order == 1:
var1 = kr_prod(X, U).T
var2 = kr_prod(var1, var1)
var3 = (tau * np.matmul(var2, ten2mat(binary_tensor, order).T).reshape([rank, rank, dim[order]])
+ np.dstack([var_Lambda_hyper] * dim[order]))
var4 = (tau * np.matmul(var1, ten2mat(sparse_tensor, order).T)
+ np.dstack([np.matmul(var_Lambda_hyper, var_mu_hyper)] * dim[order])[0, :, :])
for i in range(dim[order]):
inv_var_Lambda = inv(var3[ :, :, i])
vec = mvnrnd(np.matmul(inv_var_Lambda, var4[:, i]), inv_var_Lambda)
if order == 0:
U[i, :] = vec.copy()
elif order == 1:
V[i, :] = vec.copy()

mat0 = X[0 : np.max(time_lags), :]
mat = np.matmul(mat0.T, mat0)
new_mat = np.zeros((dim3 - np.max(time_lags), rank))
for t in range(dim3 - np.max(time_lags)):
new_mat[t, :] = (X[t + np.max(time_lags), :]
- np.einsum('ij, ij -> j', theta, X[t + np.max(time_lags) - time_lags, :]))
mat += np.matmul(new_mat.T, new_mat)
var_W_hyper = inv(inv(W0) + mat)
Lambda_x = wishart(df = dim3 + nu0, scale = var_W_hyper, seed = None).rvs()

var1 = kr_prod(V, U).T
var2 = kr_prod(var1, var1)
var3 = (tau * np.matmul(var2, ten2mat(binary_tensor, 2).T).reshape([rank, rank, dim3])
+ np.dstack([Lambda_x] * dim3))
var4 = tau * np.matmul(var1, ten2mat(sparse_tensor, 2).T)
for t in range(dim3):
Mt = np.zeros((rank, rank))
Nt = np.zeros(rank)
if t < np.max(time_lags):
Qt = np.zeros(rank)
else:
Qt = np.matmul(Lambda_x, np.einsum('ij, ij -> j', theta, X[t - time_lags, :]))
if t < dim3 - np.min(time_lags):
if t >= np.max(time_lags) and t < dim3 - np.max(time_lags):
index = list(range(0, d))
else:
index = list(np.where((t + time_lags >= np.max(time_lags)) & (t + time_lags < dim3)))[0]
for k in index:
Ak = theta[k, :]
Mt += np.multiply(np.outer(Ak, Ak), Lambda_x)
theta0 = theta.copy()
theta0[k, :] = 0
var5 = (X[t + time_lags[k], :]
- np.einsum('ij, ij -> j', theta0, X[t + time_lags[k] - time_lags, :]))
Nt += np.matmul(np.matmul(np.diag(Ak), Lambda_x), var5)
var_mu = var4[:, t] + Nt + Qt
var_Lambda = var3[:, :, t] + Mt
inv_var_Lambda = inv(var_Lambda)
X[t, :] = mvnrnd(np.matmul(inv_var_Lambda, var_mu), inv_var_Lambda)

if iters + 1 > maxiter1 - maxiter2:
U_plus += U
V_plus += V
X_plus += X

tensor_hat = cp_combine(U, V, X)
if iters + 1 > maxiter1 - maxiter2:
X_new[0 : dim3, :] = X.copy()
X_new[dim3, :] = np.einsum('ij, ij -> j', theta, X_new[dim3 - time_lags, :])
X_new_plus += X_new
tensor_hat_plus += cp_combine(U, V, X_new)
rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])

var_alpha = alpha + 0.5 * sparse_tensor[position].shape[0]
error = sparse_tensor - tensor_hat
var_beta = beta + 0.5 * np.sum(error[position] ** 2)
tau = np.random.gamma(var_alpha, 1/var_beta)

theta_bar = np.mean(theta, axis = 0)
var_mu_hyper = (d * theta_bar + beta0 * mu0)/(d + beta0)
var_W_hyper = inv(inv(W0) + cov_mat(theta) + d * beta0/(d + beta0)
* np.outer(theta_bar - mu0, theta_bar - mu0))
var_Lambda_hyper = wishart(df = d + nu0, scale = var_W_hyper, seed = None).rvs()
var_mu_hyper = mvnrnd(var_mu_hyper, inv((d + beta0) * var_Lambda_hyper))

for k in range(d):
theta0 = theta.copy()
theta0[k, :] = 0
mat0 = np.zeros((dim3 - np.max(time_lags), rank))
for L in range(d):
mat0 += np.matmul(X[np.max(time_lags) - time_lags[L] : dim3 - time_lags[L], :],
np.diag(theta0[L, :]))
VarPi = X[np.max(time_lags) : dim3, :] - mat0
var1 = np.zeros((rank, rank))
var2 = np.zeros(rank)
for t in range(np.max(time_lags), dim3):
B = X[t - time_lags[k], :]
var1 += np.multiply(np.outer(B, B), Lambda_x)
var2 += np.matmul(np.matmul(np.diag(B), Lambda_x), VarPi[t - np.max(time_lags), :])
var_Lambda = var1 + var_Lambda_hyper
inv_var_Lambda = inv((var_Lambda + var_Lambda.T)/2)
var_mu = np.matmul(inv_var_Lambda, var2 + np.matmul(var_Lambda_hyper, var_mu_hyper))
theta[k, :] = mvnrnd(var_mu, inv_var_Lambda)
if iters + 1 > maxiter1 - maxiter2:
theta_plus += theta

if (iters + 1) % 200 == 0 and iters < maxiter1 - maxiter2:
print('Iter: {}'.format(iters + 1))
print('RMSE: {:.6}'.format(rmse))
print()

U = U_plus/maxiter2
V = V_plus/maxiter2
X = X_plus/maxiter2
X_new = X_new_plus/maxiter2
theta = theta_plus/maxiter2
tensor_hat = tensor_hat_plus/maxiter2
if maxiter1 >= 100:
final_mape = np.sum(np.abs(dense_tensor[pos]
- tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]
final_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])
print('Imputation MAPE: {:.6}'.format(final_mape))
print('Imputation RMSE: {:.6}'.format(final_rmse))
print()

return tensor_hat, U, V, X_new, theta


How to transform a data set into something we can use for missing data imputation?

In [10]:
import scipy.io

dense_tensor = tensor['tensor']
rm_tensor = rm_tensor['rm_tensor']
nm_tensor = nm_tensor['nm_tensor']

missing_rate = 0.1

# =============================================================================
### Random missing (RM) scenario
### Set the RM scenario by:
binary_tensor = np.round(rm_tensor + 0.5 - missing_rate)
# =============================================================================

sparse_tensor = np.multiply(dense_tensor, binary_tensor)


Question: Given only the partially observed data $\mathcal{Y}\in\mathbb{R}^{m\times n\times f}$, how can we impute the unknown missing values?

The main influential factors for such imputation model are:

• rank.

• maxiter1.

• maxiter2.

In [11]:
import time
start = time.time()
dim1, dim2, dim3 = sparse_tensor.shape
rank = 30
time_lags = np.array([1, 2, 24])
d = time_lags.shape[0]
init = {"U": 0.1 * np.random.rand(dim1, rank),
"V": 0.1 * np.random.rand(dim2, rank),
"X": 0.1 * np.random.rand(dim3, rank),
"theta": 0.1 * np.random.rand(d, rank)}
maxiter1 = 1100
maxiter2 = 100
BTTF(dense_tensor, sparse_tensor, init, rank, time_lags, maxiter1, maxiter2)
end = time.time()
print('Running time: %d seconds'%(end - start))

Iter: 200
RMSE: 5.11937

Iter: 400
RMSE: 5.02385

Iter: 600
RMSE: 4.91347

Iter: 800
RMSE: 4.93888

Iter: 1000
RMSE: 4.9236

Imputation MAPE: 0.516337
Imputation RMSE: 4.66485

Running time: 1678 seconds

In [12]:
import scipy.io

dense_tensor = tensor['tensor']
rm_tensor = rm_tensor['rm_tensor']
nm_tensor = nm_tensor['nm_tensor']

missing_rate = 0.3

# =============================================================================
### Random missing (RM) scenario
### Set the RM scenario by:
binary_tensor = np.round(rm_tensor + 0.5 - missing_rate)
# =============================================================================

sparse_tensor = np.multiply(dense_tensor, binary_tensor)

In [13]:
import time
start = time.time()
dim1, dim2, dim3 = sparse_tensor.shape
rank = 30
time_lags = np.array([1, 2, 24])
d = time_lags.shape[0]
init = {"U": 0.1 * np.random.rand(dim1, rank),
"V": 0.1 * np.random.rand(dim2, rank),
"X": 0.1 * np.random.rand(dim3, rank),
"theta": 0.1 * np.random.rand(d, rank)}
maxiter1 = 1100
maxiter2 = 100
BTTF(dense_tensor, sparse_tensor, init, rank, time_lags, maxiter1, maxiter2)
end = time.time()
print('Running time: %d seconds'%(end - start))

Iter: 200
RMSE: 5.16748

Iter: 400
RMSE: 5.10569

Iter: 600
RMSE: 5.02703

Iter: 800
RMSE: 5.00927

Iter: 1000
RMSE: 5.01773

Imputation MAPE: 0.522777
Imputation RMSE: 4.75737

Running time: 1691 seconds

In [14]:
import scipy.io

dense_tensor = tensor['tensor']
rm_tensor = rm_tensor['rm_tensor']
nm_tensor = nm_tensor['nm_tensor']

missing_rate = 0.1

# =============================================================================
### Non-random missing (NM) scenario
### Set the NM scenario by:
binary_tensor = np.zeros(dense_tensor.shape)
for i1 in range(dense_tensor.shape[0]):
for i2 in range(dense_tensor.shape[1]):
for i3 in range(61):
binary_tensor[i1, i2, i3 * 24 : (i3 + 1) * 24] = np.round(nm_tensor[i1, i2, i3] + 0.5 - missing_rate)
# =============================================================================

sparse_tensor = np.multiply(dense_tensor, binary_tensor)

In [15]:
import time
start = time.time()
dim1, dim2, dim3 = sparse_tensor.shape
rank = 30
time_lags = np.array([1, 2, 24])
d = time_lags.shape[0]
init = {"U": 0.1 * np.random.rand(dim1, rank),
"V": 0.1 * np.random.rand(dim2, rank),
"X": 0.1 * np.random.rand(dim3, rank),
"theta": 0.1 * np.random.rand(d, rank)}
maxiter1 = 1100
maxiter2 = 100
BTTF(dense_tensor, sparse_tensor, init, rank, time_lags, maxiter1, maxiter2)
end = time.time()
print('Running time: %d seconds'%(end - start))

Iter: 200
RMSE: 5.09945

Iter: 400
RMSE: 5.06215

Iter: 600
RMSE: 5.03309

Iter: 800
RMSE: 5.00044

Iter: 1000
RMSE: 4.99775

Imputation MAPE: 0.524108
Imputation RMSE: 4.77874

Running time: 1673 seconds

In [16]:
import scipy.io

dense_tensor = tensor['tensor']
rm_tensor = rm_tensor['rm_tensor']
nm_tensor = nm_tensor['nm_tensor']

missing_rate = 0.3

# =============================================================================
### Non-random missing (NM) scenario
### Set the NM scenario by:
binary_tensor = np.zeros(dense_tensor.shape)
for i1 in range(dense_tensor.shape[0]):
for i2 in range(dense_tensor.shape[1]):
for i3 in range(61):
binary_tensor[i1, i2, i3 * 24 : (i3 + 1) * 24] = np.round(nm_tensor[i1, i2, i3] + 0.5 - missing_rate)
# =============================================================================

sparse_tensor = np.multiply(dense_tensor, binary_tensor)

In [17]:
import time
start = time.time()
dim1, dim2, dim3 = sparse_tensor.shape
rank = 30
time_lags = np.array([1, 2, 24])
d = time_lags.shape[0]
init = {"U": 0.1 * np.random.rand(dim1, rank),
"V": 0.1 * np.random.rand(dim2, rank),
"X": 0.1 * np.random.rand(dim3, rank),
"theta": 0.1 * np.random.rand(d, rank)}
maxiter1 = 1100
maxiter2 = 100
BTTF(dense_tensor, sparse_tensor, init, rank, time_lags, maxiter1, maxiter2)
end = time.time()
print('Running time: %d seconds'%(end - start))

Iter: 200
RMSE: 5.35638

Iter: 400
RMSE: 5.25065

Iter: 600
RMSE: 5.21765

Iter: 800
RMSE: 5.20741

Iter: 1000
RMSE: 5.13667

Imputation MAPE: 0.527793
Imputation RMSE: 4.88569

Running time: 1675 seconds


Experiment results of missing data imputation using Bayesian Temporal Tensor Factorization (BTTF):

scenario rank maxiter1 maxiter2 mape rmse
0.1, RM 30 1100 100 0.5163 4.6649
0.3, RM 30 1100 100 0.5228 4.7574
0.1, NM 30 1100 100 0.5241 4.7787
0.3, NM 30 1100 100 0.5278 4.8857