y_hat = model_persistence(x)
predictions.append(y_hat)
submission_generator.generate(predictions)
test_score = mean_absolute_error(test_y, predictions)
print('Test MAE: %.3f' % test_score)
# plot predictions vs expected
plt.plot(test_y, label="real values")
plt.plot(predictions, color='red', label="predictions")
plt.legend(loc='upper left')
plt.show()
# Implementing Auto Regression Model
# Training Autoregression
print(train[:, 1])
model = AR(train_y)
model_fit = model.fit()
print('Lag: %s' % model_fit.k_ar)
print('Coefficients: %s' % model_fit.params)
# Making predictions
predictions = model_fit.predict(start=len(train_y), end=len(train_y) + len(test_y) - 1, dynamic=False)
error = mean_absolute_error(test_y, predictions)
print('Test MAE: %.3f' % error)
# Plotting results
plt.plot(test_y, label="real values")
plt.plot(predictions, color='red', label="predictions")
plt.legend(loc='upper left')
plt.show()
def test_ar_select_order():
# 2118
np.random.seed(12345)
y = sm.tsa.arma_generate_sample([1, -0.75, 0.3], [1], 100)
ts = Series(y, index=DatetimeIndex(start="1/1/1990", periods=100, freq="M"))
ar = AR(ts)
res = ar.select_order(maxlag=12, ic="aic")
assert_(res == 2)
def test_ar_select_order():
# 2118
np.random.seed(12345)
y = sm.tsa.arma_generate_sample([1, -.75, .3], [1], 100)
ts = TimeSeries(y, index=DatetimeIndex(start='1/1/1990', periods=100,
freq='M'))
ar = AR(ts)
res = ar.select_order(maxlag=12, ic='aic')
assert_(res == 2)
def test_ar_select_order_tstat():
rs = np.random.RandomState(123)
tau = 25
y = rs.randn(tau)
ts = Series(y, index=date_range(start='1/1/1990', periods=tau,
freq='M'))
ar = AR(ts)
res = ar.select_order(maxlag=5, ic='t-stat')
assert_equal(res, 0)
def predict(x, params):
x = delete6keep1(x)
#x = range(10)
try:
model = AR(x)
res = model.fit(maxlag=1)
ret = int(res.predict(len(x), len(x))[0])
if ret>100:
print x,ret
return ret
except Exception, err:
return 0
def transform(self, X):
"""
Detect and remove dropped.
"""
out = []
for x in X:
tmp = []
for a in x:
ar_mod = AR(a[::self.subsample])
ar_res = ar_mod.fit(self.order)
bse = ar_res.bse
if len(bse)!=(self.order + 1):
bse = np.array([np.nan] * (self.order + 1))
tmp.append(bse)
out.append(tmp)
return np.array(out)
def project(ser, start, end):
"""Fit AR model to series and project to end of index. Primarily
useful for filling in missing values at the end of time series to
ensure they match.
ser: series to fit trend to
start: date to begin fitting
end: date to end fitting
Returns:
new_ser: series with missing end values replaced by fitted
values."""
from statsmodels.tsa.ar_model import AR
trend_mod = AR(ser[start:end]).fit()
return trend_mod.predict(
start=trend_mod.k_ar, end=ser.index.shape[0])
def __call__(self, sample):
"""
Computes self.n_coef AR coefficients for an array of samples
See https://en.wikipedia.org/wiki/Autoregressive_model
@param sample: m x n numpy array, m -- number of samples, n -- length of each sample
@return: m x self.n_coef numpy array containing AR coefficients for each sample
"""
m = sample.shape[0]
trend = 'c' if self.use_constant else 'nc'
maxlag = self.n_coef - 1 if self.use_constant else self.n_coef
features = []
for i in xrange(m):
model = AR(sample[i])
results = model.fit(maxlag, trend=trend)
features.append(results.params)
return np.array(features)
def AutoRegression(train, test):
model = AR(train)
model_fit = model.fit()
window = model_fit.k_ar
coef = model_fit.params
# walk forward over time steps in test
history = train[len(train) - window:]
# print(len(history))
history = [history[i] for i in range(len(history))]
# print(history[0:5])
predictions = list()
for t in range(len(test)):
length = len(history)
lag = [history[i] for i in range(length - window, length)]
yhat = coef[0]
for d in range(window):
yhat += coef[d + 1] * lag[window - d - 1]
obs = test[t]
predictions.append(yhat)
history.append(obs) # new observations added to history
# print('predicted=%f, expected=%f' % (yhat, obs))
return predictions
def spectrum0_ar(x):
z = np.arange(1, len(x) + 1)
z = z[:, np.newaxis]**[0, 1]
p, res, rnk, s = lstsq(z, x)
residuals = x - np.matmul(z, p)
if residuals.std() == 0:
spec = order = 0
else:
ar_out = AR(x).fit(ic='aic', trend='c')
order = ar_out.k_ar
spec = np.var(ar_out.resid) / (1 - np.sum(ar_out.params[1:]))**2
return spec, order
def sentiment_prediction(data, user):
y_train = data["sentiments"]
model = AR(y_train)
model_fit = model.fit(maxlag=1)
future_pred = model_fit.predict(start=len(data["sentiments"]),
end=105,
dynamic=False)
fig = go.Figure()
fig.add_trace(
go.Scatter(y=data['sentiments'],
mode='lines+markers',
name='past sentiment',
text=(data['time'])))
fig.add_trace(
go.Scatter(y=future_pred,
x=list(range(len(data["sentiments"]), 105)),
mode='lines+markers',
name='prediction of future sentiment',
text=(data['time'])))
fig.update_layout(
title=f"Sentiment Analysis of @{user} twitter interactions")
fig.show()
def autoRegression3(day):
col_daily = db['daily']
dailyGrossSet = []
for y in range(2008, 2018):
for record in col_daily.find({"Year": y}):
movieNumber = record['MoviesTracked']
gross = record['Gross($)'].replace(",", "")
dailyGrossSet.append(int(gross) / int(movieNumber))
daycount = 0
for record in col_daily.find({"Year": 2018}):
movieNumber = record['MoviesTracked']
gross = record['Gross($)'].replace(",", "")
dailyGrossSet.append(int(gross) / int(movieNumber))
daycount += 1
if daycount >= day:
break
print(dailyGrossSet)
# fit model
model = AR(dailyGrossSet)
model_fit = model.fit()
# make prediction
res = model_fit.predict(len(dailyGrossSet), len(dailyGrossSet))
print(res)
def test_mle(self):
# check predict with no constant, #3945
res1 = self.res1
endog = res1.model.endog
res0 = AR(endog).fit(maxlag=9, method='mle', trend='nc', disp=0)
assert_allclose(res0.fittedvalues[-10:],
res0.fittedvalues[-10:],
rtol=0.015)
res_arma = ARMA(endog, (9, 0)).fit(method='mle', trend='nc', disp=0)
assert_allclose(res0.params, res_arma.params, atol=5e-6)
assert_allclose(res0.fittedvalues[-10:],
res_arma.fittedvalues[-10:],
rtol=1e-4)
def metodo_Dm(cpu_workload, Y, Z, output_list):
X = Y
train_size = int(len(X))
train, test = X[:train_size], X[:train_size]
#print(len(train)," ",len(test))
#print("test = ",len(test))
# train autoregression
model = AR(train)
model_fit = model.fit()
window = model_fit.k_ar
coef = model_fit.params
# walk forward over time steps in test
history = train[len(train) - window:]
history = [history[i] for i in range(len(history))]
predictions = list()
print(len(Z), " ", len(output_list), " ", len(test))
for t in range(len(test)):
length = len(history)
lag = [history[i] for i in range(length - window, length)]
yhat = coef[0]
for d in range(window):
yhat += coef[d + 1] * lag[window - d - 1]
obs = test[t]
if (Z[t] == output_list[0]
): #or Z[t]==output_list[1] or Z[t]==output_list[2]):
predictions.append(cpu_workload[t])
else:
predictions.append(-yhat + 4)
history.append(obs)
#print('predicted=%f, expected=%f' % (yhat, obs))
error = mean_squared_error(test, predictions)
return test, predictions, error
def autoregression(data, train_test_percentage=20):
train_test_size = int(len(data) * float(train_test_percentage) / 100)
train, test = data[0:train_test_size], data[train_test_size:]
# train autoregression
model = AR(train)
model_fit = model.fit()
window = model_fit.k_ar
coef = model_fit.params
# walk forward over time steps in test
history = train[len(train)-window:]
history = [history[i] for i in range(len(history))]
predictions = list()
for t in range(len(test)):
length = len(history)
lag = [history[i] for i in range(length-window, length)]
yhat = coef[0]
for d in range(window):
yhat += coef[d+1] * lag[window-d-1]
obs = test[t]
predictions.append(yhat)
history.append(obs)
mse_error = mean_squared_error(test, predictions)
print 'Autoregression MSE: '+ str(mse_error)
pyplot.plot(range(len(test)), predictions, color='red', lw=2, label='prediction')
pyplot.plot(range(len(test)), test, color='green', lw=2, label='actual')
pyplot.ylabel('max temp')
pyplot.xlabel('days from 1/1/2009')
pyplot.title('Autoregression')
pyplot.show()
return predictions
def modelling_AR(df, name):
"""
Function to get the prediction model AR and apply to our DF
"""
data_close = df[f'CLOSE_{name}']
b, a = signal.butter(3, 1/10)
filtrd_data_close = signal.filtfilt(b, a, data_close)
df2 = pd.DataFrame({"X":data_close.to_numpy(),"Xf": filtrd_data_close},index=df.index)
dr = df2.index
realidad = df2.loc[dr[:22808]]
futuro = df2.loc[dr[22808:]]
predictions_AR = dict()
for col in realidad.columns:
train = realidad[col]
test = futuro[col]
# Entrena el modelo AR
model_AR = AR(train)
print(f"Entrenando con los datos desde la serie {col}")
model_fit_AR = model_AR.fit(maxlag=4)
# Predice los valores AR
predictions_AR[f'{col}_prediction'] = model_fit_AR.predict(start=len(train),
end=len(train)+len(test)-1, dynamic=False)
pred_AR = pd.DataFrame(predictions_AR)
pred_AR.index = futuro.index
AR_predictions = pd.DataFrame({
"GT":futuro.X,
"X":pred_AR.X_prediction,
"Xf":pred_AR.Xf_prediction,
"diff_X": futuro.X - pred_AR.X_prediction,
"diff_Xf":futuro.X - pred_AR.Xf_prediction},index=futuro.index)
return AR_predictions
def AutoRegressive(self, data, testSize=2, test=True):
# Autoregressive model used for time-series predictions
# if test= True, then select the last testSize points as test set
# else predict for a period of testSize
print(data.shape)
if test:
trainData = data[:-testSize]
testData = data[-testSize:]
else:
trainData = data
model = AR(trainData)
modelFit = model.fit()
winSize, coeff = modelFit.k_ar, modelFit.params
predData = list(trainData[-winSize:])
pred = []
for i in range(testSize):
x = list(predData[-winSize:])
y = coeff[0]
# use winSize number of data to predict future value
for n in range(winSize):
y += coeff[n + 1] * x[winSize - (n + 1)]
if test:
# use test data to predict future value
predData.append(testData[i])
else:
# use predicted value to predict future value
predData.append(y)
pred.append(y)
if test:
error = mse(testData, pred)
return pred, error, testData
else:
error = None
return pred, error
def time_series(ts_dict, num_pred=7, title="Efficiency"):
'''Models and predicts time series from data'''
data = []
for k in ts_dict:
ts = ts_dict[k]
train, test = ts[1:len(ts) - num_pred], ts[len(ts) - num_pred:]
# train autoregression
model = AR(train, freq="W")
model_fit = model.fit()
# make predictions
predictions = model_fit.predict(start=len(train),
end=len(train) + len(test) - 1,
dynamic=False)
# Create a trace results
predict = pd.concat([ts[len(ts) - 8:len(ts) - 7], predictions])
line_predict = go.Scatter(x=predict.index,
y=predict.values,
name="prediccion " +
k) # , marker={'color': 'rgb(0,255,0)'})
line_hist = go.Scatter(x=train.index,
y=train.values,
name="historicos " + k)
data += [line_hist, line_predict]
layout = go.Layout(title=title)
figure = go.Figure(data=data, layout=layout)
return ({
'figure': figure,
'curr_val': train[-1],
'first_pred': predictions[0]
})
def fit_ar(outputs, inputs, guessed_dim):
"""Fits an AR model of order p = guessed_dim.
Args:
outputs: Array with the output values from the LDS.
inputs: Array with exogenous inputs values.
guessed_dim: Guessed hidden dimension.
Returns:
- Fitted AR coefficients.
"""
if outputs.shape[1] > 1:
# If there are multiple output dimensions, fit autoregressive params on
# each dimension separately and average.
params_list = [
fit_ar(outputs[:, j:j+1], inputs, guessed_dim) \
for j in xrange(outputs.shape[1])]
return np.mean(np.concatenate([a.reshape(1, -1) for a in params_list]),
axis=0)
if inputs is None:
model = AR(outputs).fit(ic='bic',
trend='c',
maxlag=guessed_dim,
disp=0)
arparams = np.zeros(guessed_dim)
arparams[:model.k_ar] = model.params[model.k_trend:]
return arparams
else:
model = ARMA(outputs, order=(guessed_dim, 0), exog=inputs)
try:
arma_model = model.fit(start_ar_lags=guessed_dim,
trend='c',
disp=0)
return arma_model.arparams
except (ValueError, np.linalg.LinAlgError) as e:
warnings.warn(str(e), sm_exceptions.ConvergenceWarning)
return np.zeros(guessed_dim)
def test_ar_errors(reset_randomstate):
with pytest.raises(ValueError, match='Only the univariate case'):
with pytest.warns(FutureWarning):
AR(np.empty((1000, 2)))
with pytest.warns(FutureWarning):
ar = AR(np.random.standard_normal(1000))
with pytest.raises(ValueError, match='Method yw not'):
ar.fit(method='yw')
with pytest.raises(ValueError, match='ic option fpic not'):
ar.fit(ic='fpic')
res = ar.fit()
with pytest.raises(ValueError, match='Start must be >= k_ar'):
res.predict(start=0)
with pytest.raises(ValueError, match='Prediction must have `end` after'):
res.predict(start=100, end=99)
with pytest.raises(ValueError, match='Length of start params'):
with pytest.warns(FutureWarning):
AR(np.random.standard_normal(1000)).fit(maxlag=2,
method='mle',
start_params=[1, 1])
def test_ar_errors(reset_randomstate):
with pytest.raises(ValueError, match="Only the univariate case"):
with pytest.warns(FutureWarning):
AR(np.empty((1000, 2)))
with pytest.warns(FutureWarning):
ar = AR(np.random.standard_normal(1000))
with pytest.raises(ValueError, match="Method yw not"):
ar.fit(method="yw")
with pytest.raises(ValueError, match="ic option fpic not"):
ar.fit(ic="fpic")
res = ar.fit()
with pytest.raises(ValueError, match="Start must be >= k_ar"):
res.predict(start=0)
with pytest.raises(ValueError, match="Prediction must have `end` after"):
res.predict(start=100, end=99)
with pytest.raises(ValueError, match="Length of start params"):
with pytest.warns(FutureWarning):
AR(np.random.standard_normal(1000)).fit(maxlag=2,
method="mle",
start_params=[1, 1])
class TestingActivity4_01(unittest.TestCase):
def setUp(self) -> None:
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
self.data = pd.read_csv(
os.path.join(ROOT_DIR, '..', 'Datasets', 'austin_weather.csv'))
def test_AR(self):
self.model = AR(self.data.TempAvgF)
self.model_fit = self.model.fit()
self.max_lag = self.model_fit.k_ar
self.assertEqual(self.max_lag, (23))
self.params = self.model_fit.params[0:4]
self.assertEqual(round(self.params[0], 4), (1.9094))
self.assertEqual(round(self.params[1], 4), (0.9121))
def test_mle(self):
# check predict with no constant, #3945
res1 = self.res1
endog = res1.model.endog
with pytest.warns(FutureWarning):
res0 = AR(endog).fit(maxlag=9, method="mle", trend="nc", disp=0)
assert_allclose(res0.fittedvalues[-10:],
res0.fittedvalues[-10:],
rtol=0.015)
res_arma = ARIMA(endog, order=(9, 0, 0), trend="n").fit()
assert_allclose(res0.params, res_arma.params[:-1], rtol=1e-2)
assert_allclose(res0.fittedvalues[-10:],
res_arma.fittedvalues[-10:],
rtol=1e-4)
def get_stat_AR_coefficients(self, signals, max_lag):
"""Get the auto-regression coefficients for a set of time series signals.
Args:
signals (DataFrame): A Pandas DataFrame of waveforms, one per column
max_lag (float): The maximum number of AR coefficients to return. Will be zero padded if model requires
less than the number specified.
Returns DataFrame: A dataframe that contains a single row where each column is a parameter coefficient.
"""
for i in range(0, np.shape(signals)[1]):
# The AR model throws for some constant signals. The signals should have been normalized into z-scores, in
# which case the parameters for an all zero signal are all zero.
if self.is_constant_signal(signals[i]) and signals[0, i] == 0:
parameters = np.append((np.zeros(max_lag + 1)))
else:
model = AR(signals[:, i])
model_fit = model.fit(maxlag=max_lag, ic=None)
if np.shape(model_fit.params)[0] < max_lag + 1:
parameters = np.pad(
model_fit.params,
(0, max_lag + 1 - np.shape(model_fit.params)[0]),
'constant',
constant_values=0)
elif np.shape(model_fit.params)[0] > max_lag + 1:
parameters = model_fit.params[:max_lag]
else:
parameters = model_fit.params
if i == 0:
coefficients = parameters
else:
coefficients = np.append(coefficients, parameters, axis=0)
return pd.DataFrame(coefficients).T
def returnpred(p, m, file='SIH.csv'):
dataset = pd.read_csv(file)
x1 = dataset.loc[(dataset['Product_Name'] == p) & (dataset['Month'] == m)]
y1 = x1.groupby('Day').mean()
y1 = y1.rename(columns={'Month': 'days'})
y = y1.iloc[:, 5]
n1 = len(y)
train1 = y[0:25]
test1 = y[25:n1]
model_AR = AR(train1)
model_fit_AR = model_AR.fit()
predictions_AR = model_fit_AR.predict(start=25, end=n1 + 10)
plt.figure()
plt.plot(test1)
plt.plot(predictions_AR, color='red')
plt.title("Future Predictions of different company")
plt.legend(['Original', 'Predictions'])
fig = plt.gcf()
plotly_fig = tls.mpl_to_plotly(fig)
plotly_fig['layout']['width'] = 1200
plot_div = plot(plotly_fig, output_type='div', include_plotlyjs=False)
return plot_div
class TestingExercise4_01(unittest.TestCase):
def setUp(self) -> None:
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
self.data = pd.read_csv(
os.path.join(ROOT_DIR, '..', 'Datasets', 'spx.csv'))
def test_AR(self):
self.model = AR(self.data.close)
self.model_fit = self.model.fit()
self.max_lag = self.model_fit.k_ar
self.assertEqual(self.max_lag, (36))
self.params = self.model_fit.params[0:4]
self.assertEqual(round(self.params[0], 4), (0.1142))
self.assertEqual(round(self.params[1], 4), (0.9442))
def main(csv_file_path):
# load csv
all_samples = load_csv(csv_file_path)
# split to test and train
train, test = split_samples(all_samples)
# set history=train (duplicate train)
history = list(train)
# for i < number_of_predictions
prediction_list = list()
for prediction_index in range(PREDICTIONS):
# train model on history
model = AR(history)
model_fit = model.fit()
# predict next value and concatenate to prediction list
predictions = model_fit.predict_using_learned_params(
start=len(history), end=len(history), dynamic=False)
prediction_list.append(predictions[0])
# concatenate test[i] to history
history.append(test[prediction_index])
print('predicted={pred_value}, expected={real_value}'.format(
pred_value=prediction_list[-1], real_value=test[prediction_index]))
# keep history to same length
history = history[1:]
# calculate MSE with test and prediction lists
error = mean_squared_error(test, prediction_list)
print('Test MSE = {mse_value}'.format(mse_value=error))
# return test and predictions
return test, prediction_list
def AR_prediction(data, test_data, test_for_AR, ar_summary):
print("AR_prediction() start execute")
tickers = test_for_AR
log_returns = to_log_return(data)
with open('prediction_results/AR_model_prediction.txt',
'w') as results_file:
for ticker in tickers:
print("AR_prediction() start execute in ticker: " + ticker)
log_rtn = log_returns[ticker].dropna()
result = AR(log_rtn).fit(ar_summary[ticker][0])
result_show = result.predict(test_data.index[0],
test_data.index[-1])
test_log_returns = to_log_return(test_data)
test_log_rtn = test_log_returns[ticker].dropna()
test_log_rtn = test_log_rtn[result_show.index]
visualization(test_log_rtn, result_show, 'AR_prediction', ticker)
rmse = sqrt(
sum((result_show - test_log_rtn).dropna()**2) /
test_log_rtn.size)
message = "The prediction for {} is \n {}\n RMSE:{}\n".format(
ticker, result_show, rmse)
results_file.write(message)
return
def OU_fitting(series):
# series: pd.Series, indexed by date
# return the fitted OU process model params.
ar_model = AR(endog=series).fit(maxlag=1)
[b, a] = ar_model.params.tolist()
resid_std = np.std(ar_model.resid)
lam = -np.log(a)
mu = b / (1 - a)
sigma = resid_std * np.sqrt(-2 * np.log(a) / (1 - a * a))
res = {'ar_model': ar_model, 'lam': lam, 'mu': mu, 'sigma': sigma}
return (res)
def ar_coefficient(x, c, param):
"""
This feature calculator fit the unconditional maximum likelihood of an autoregressive AR(k) process. The k parameter
is the maximum lag of the process
.. math::
X_{t}=\\varphi_0 +\\sum _{{i=1}}^{k}\\varphi_{i}X_{{t-i}}+\\varepsilon_{t}
For the configurations from param which should contain the maxlag "k" and such an AR process is calculated. Then
the coefficients :math:`\\varphi_{i}` whose index :math:`i` contained from "coeff" are returned.
:param x: the time series to calculate the feature of
:type x: pandas.Series
:param c: the time series name
:type c: str
:param param: contains dictionaries {"coeff": x, "k": y} with x,y int
:type param: list
:return x: the different feature values
:return type: pandas.Series
"""
df_cfg = pd.DataFrame(param)
df_cfg["k"] = df_cfg["k"].apply(int)
res = pd.Series()
for k in df_cfg["k"].unique():
coeff = df_cfg[df_cfg["k"] == k]["coeff"]
try:
mod = AR(list(x)).fit(maxlag=k, solver="mle").params
res_tmp = pd.Series(index=["{}__ar_coefficient__k_{}__coeff_{}".format(c, k, p) for p in coeff])
for p in coeff:
if p <= k:
try:
res_tmp["{}__ar_coefficient__k_{}__coeff_{}".format(c, k, p)] = mod[p]
except IndexError:
res_tmp["{}__ar_coefficient__k_{}__coeff_{}".format(c, k, p)] = 0
else:
res_tmp["{}__ar_coefficient__k_{}__coeff_{}".format(c, k, p)] = np.NaN
except (LinAlgError, ValueError):
res_tmp = pd.Series([np.NaN] * len(coeff),
index=["{}__ar_coefficient__k_{}__coeff_{}".format(c, k, p) for p in coeff])
res = res.append(res_tmp)
return res
def __init__(self, train, test, last_train, alpha, diff):
"""
The class is initialized to transfored external parameters into class properties.
"""
self.train, self.test, self.last_train = train, test, last_train
self.models_init, self.models_param = dict(), dict()
k = len(last_train)
self.idx_orig = self.train.columns[:-k]
self.idx_trans = self.train.columns[-k:]
self.alpha = alpha
self.diff = diff
for i in self.idx_trans:
self.models_init[i] = AR(self.train[i].values)
def predict_AR(df, window=ROLLING_WINDOW, p=1):
"""第一种方法： 在给定滚动周期下利用AR(P)模型预测
输入:
df:DataFrame, 波动率原始数据
window: 整数滚动周期
p: int, lag of AR model
输出:
vols_pred: 时间序列, 预测波动率
"""
fit = lambda x: AR(x).fit(maxlag=p, disp=0).predict(start=x.size,
end=x.size)
vols_pred = df[VOL_NAME].rolling(window).apply(fit)
vols_pred.name = 'AR' + '_' + repr(window) + '_' + repr(p)
print(vols_pred.name + " prediction finished.")
return vols_pred
def initialize(df):
global predictions
predictions = pd.DataFrame()
user_input = input(
"Do you want to forecast by CATEGORY, VENDOR or PRODUCT? Select one: ")
user_input = user_input.upper()
user_selection = input('Type down your specific' + ' ' + user_input +
' in the same format' + ':')
dataset = df[['INV_AMOUNT_USD', user_input, 'Date']]
dataset = dataset.groupby([user_input, 'Date']).sum().reset_index()
sample = dataset.loc[dataset[user_input] == user_selection][[
'INV_AMOUNT_USD', 'Date'
]]
FBProphet(sample)
AR(sample)
ARIMA(sample)
expo_smooth(sample)
def predict_AR(array, p=1):
"""第一种方法： 在给定滚动周期下利用AR(P)模型预测
输入:
df:DataFrame, 波动率原始数据
window: 整数滚动周期
p: int, lag of AR model
输出:
vols_pred: 时间序列, 预测波动率
"""
#fit = lambda x: AR(x).fit(maxlag=p, disp=0).predict(start=x.size, end=x.size)
#vols_pred = df[VOL_NAME].rolling(window).apply(fit)
vols_pred = AR(array).fit(maxlag=p, disp=0).predict(start=array.size,
end=array.size,
dynamic=True)
return vols_pred
def ARC_cal(input_file):
#Auto Regression Coefficient
#https://machinelearningmastery.com/autoregression-models-time-series-forecasting-python/
import pandas as pd
from pandas import Series
from matplotlib import pyplot
from statsmodels.tsa.ar_model import AR #conda install statsmodels
from sklearn.metrics import mean_squared_error #pip install -U scikit-learn scipy matplotlib #conda installe scikit-learn
import numpy as np
with open(input_file) as file_var:
for line in file_var:
channel_numbers = len(line.split()) #actually 8 as already known
break
#print("Channel number:", channel_numbers)
'''
number_of_lines = 0
with open(input_file) as file_var:
for line in file_var:
number_of_lines += 1
#print("Line number:", number_of_lines)
'''
ARC_list = []
ARC_list_LagNumber = []
'''
for column_n in range(channel_numbers):
ARC_list.append([])
'''
input_series = pd.read_csv(input_file, header=None)
input_tmp_file = input_series.values
number_of_lines = len(input_tmp_file)
input_file_array = [[
float(ele) for ele in input_tmp_file[line_n][0].split()
] for line_n in range(number_of_lines)]
input_file_array = np.array(input_file_array)
for column_n in range(channel_numbers):
model_fit = AR(input_file_array[:, column_n]).fit()
ARC_list_LagNumber.append(model_fit.k_ar)
ARC_list.append(model_fit.params.tolist())
return ARC_list_LagNumber, ARC_list
def test_summary_corner():
data = sm.datasets.macrodata.load_pandas().data["cpi"].diff().dropna()
dates = period_range(start='1959Q1', periods=len(data), freq='Q')
data.index = dates
res = AR(data).fit(maxlag=4)
summ = res.summary().as_text()
assert 'AR(4)' in summ
assert 'L4.cpi' in summ
assert '03-31-1959' in summ
res = AR(data).fit(maxlag=0)
summ = res.summary().as_text()
assert 'const' in summ
assert 'AR(0)' in summ
def forecasting(model_name, resultsDict, predictionsDict, df, df_training,
df_testcase):
#index = len(df_training)
yhat = list()
for t in tqdm(range(len(df_testcase.Ambient_Temp))):
temp_train = df[:len(df_training) + t]
if model_name == "SES":
model = SimpleExpSmoothing(temp_train.Ambient_Temp)
elif model_name == "HWES":
model = ExponentialSmoothing(temp_train.Ambient_Temp)
elif model_name == "AR":
model = AR(temp_train.Ambient_Temp)
# elif model_name == "MA":
# model = ARMA(temp_train.Ambient_Temp, order=(0, 1))
# elif model_name == "ARMA":
# model = ARMA(temp_train.Ambient_Temp, order=(1, 1))
elif model_name == "ARIMA":
model = ARIMA(temp_train.Ambient_Temp, order=(1, 0, 0))
elif model_name == "SARIMAX":
model = SARIMAX(temp_train.Ambient_Temp,
order=(1, 0, 0),
seasonal_order=(0, 0, 0, 3))
model_fit = model.fit()
if model_name == "SES" or "HWES":
predictions = model_fit.predict(start=len(temp_train),
end=len(temp_train))
elif model_name == "AR" or "ARIMA" or "SARIMAX":
predictions = model_fit.predict(start=len(temp_train),
end=len(temp_train),
dynamic=False)
yhat = yhat + [predictions]
yhat = pd.concat(yhat)
resultsDict[model_name] = metrics.evaluate(df_testcase.Ambient_Temp,
yhat.values)
predictionsDict[model_name] = yhat.values
plt.plot(df_testcase.Ambient_Temp.values, label='Original')
plt.plot(yhat.values, color='red', label=model_name + ' predicted')
plt.legend()
plt.show()
class AutoRegression(PredictionModel):
def __init__(self):
super(AutoRegression, self).__init__()
self.model = None
self.model_fitted = None
self.train_df = None
def train(self, predict_state, predict_field):
self.train_df = self.assign_train_df(predict_field)
self.state = predict_state
self.model = AR(self.train_df[predict_state])
self.model_fitted = self.model.fit(maxlag=1)
def predict(self):
pred = self.model_fitted.predict(start=len(self.train_df[self.state]),
end=len(self.train_df[self.state]) +
FUTURE_DAYS - 1,
dynamic=False)
return np.round(pred, 0).astype(np.int32).array
def ARcast(data,time,dt=False,axis=-1,missing=0):
"""
Forecast the data by using AutoRegressive method.
The code automatically find the unevenly sampled data point,
and then forecast the that point by using AR method.
Parameters
----------
data : ~numpy.ndarray
n dimensional data.
Data must have the same number of elements to the time.
time : astropy.time.core.Time
The time for the each data points.
dt : (optional) float
An Interval of the time between each data in second unit.
axis : (optional) int
An axis to forecast.
missing : (optional) float
The missing value of the data.
It may be due to data alignment.
Returns
-------
ARdata : ~numpy.ndarray
Autoregressived data.
It must be larger elements then input data.
tf : ~numpy.ndarray
Time the forecasted ARdata points.
Notes
-----
Input time must be the astropy.time.core.Time,
but output time is the ~numpy.ndarray.
References
----------
`AR model <https://en.wikipedia.org/wiki/Autoregressive_model>`_.\n
`statsmodels.tsa.ar_model.AR <http://statsmodels.sourceforge.net/devel/generated/statsmodels.tsa.ar_model.AR.html>`_.
Example
-------
>>> from fisspy.analysis.forecast import ARcast
>>> ARdata, tf = ARcast(data,t,dt=20.,axis=1)
"""
if not dt:
dt=(time[1]-time[0]).value
shape=list(data.shape)
shape0=list(data.shape)
if shape[axis]!=len(time):
raise ValueError('The size of data is different from the size of time.')
t=(time-time[0])*24*3600
t=t.value
tf=np.arange(t[0],t[-1],dt,dtype=float)
interp=interp1d(t,data,axis=axis)
datai=interp(tf)
shape.pop(axis)
ind=[shape0.index(i) for i in shape]
ind=[axis]+ind
datat=datai.transpose(ind)
shapei=datat.shape
datat=datat.reshape((shapei[0],np.prod(shapei[1:])))
shapet=datat.shape
td=t-np.roll(t,1)
addi=np.where(td >= dt*2)[0]
for wh in addi:
for i in range(shapet[1]):
y=datat[:,i]
wh2=wh+int(td[wh]/dt-1)
if (y==missing).sum()<4:
bar=AR(y)
car=bar.fit()
dar=car.predict(int(wh),int(wh2))
datat[wh:wh2+1,i]=dar
else:
datat[wh:wh2+1,i]=missing
datat=datat.reshape((shapei))
return datat.transpose(ind), tf
def get_lpc(trame):
ar_mod = AR(trame)
ar_res = ar_mod.fit(20)
return ar_res.params
return numpy.array(diff)
# Make a prediction give regression coefficients and lag obs
def predict(coef, history):
yhat = coef[0]
for i in range(1, len(coef)):
yhat += coef[i] * history[-i]
return yhat
series = Series.from_csv('../data/nifty.csv', header=0)
# split dataset
X = difference(series.values)
size = int(len(X) * 0.66)
train, test = X[0:size], X[size:]
# train autoregression
model = AR(train)
model_fit = model.fit(maxlag=6, disp=False)
window = model_fit.k_ar
coef = model_fit.params
# walk forward over time steps in test
history = [train[i] for i in range(len(train))]
predictions = list()
for t in range(len(test)):
yhat = predict(coef, history)
obs = test[t]
predictions.append(yhat)
history.append(obs)
error = mean_squared_error(test, predictions)
print('Test MSE: %.3f' % error)
# plot
pyplot.plot(test)
def test_ar_select_order_tstat():
rs = np.random.RandomState(123)
tau = 25
y = rs.randn(tau)
ts = Series(y, index=DatetimeIndex(start="1/1/1990", periods=tau, freq="M"))
ar = AR(ts)
res = ar.select_order(maxlag=5, ic="t-stat")
assert_equal(res, 0)
