def predict_model(timestamp, value, args):
"""预测主函数"""
dta = handle_data.data_to_datetimeindex(timestamp, value)
# 历史数据的间隔
interval = timestamp[1] - timestamp[0]
if len(dta) > 2 * args.predict_time and len(
dta[np.isnan(dta)]) != len(dta):
# 平滑处理
smooth_data = handle_data.diff_smooth(dta, interval)
# 周期性检测
# 具有周期性
period_result = period_check.period_check(dta, interval)
print('The result of period is %s' % period_result)
if period_result == 'yes':
try:
# 周期性分解
decomposition = seasonal_decompose(smooth_data,
two_sided=False)
except:
print('The freq of series is not supported.')
return
# 用treand部分进行预测
result = period_predict(decomposition, args, interval)
if result is not None:
return result.values
else:
print('The result of prediction os None')
return
# 不具有周期性
else:
model = models.create(args.model_name,
predict_time=args.predict_time)
train_model = model.train(smooth_data, smooth_data.index.time,
smooth_data.values)
predict_data = []
if train_model is not None:
predict_data = model.predict(train_model, smooth_data.values)
else:
print('The result of prediction os None')
return
return predict_data
def period_predict(decomposition, args, interval):
"""具有周期性时间序列的预测."""
trend = decomposition.trend
seasonal = decomposition.seasonal
trend.dropna(inplace=True)
model = models.create(args.model_name, predict_time=args.predict_time)
train_model = model.train(trend, trend.index.time, trend.values)
predict_data = []
if train_model is not None:
predict_data = model.predict(train_model, trend.values)
'''
预测新数据
'''
interval = str(interval / 60) + 'min'
# 生成长度为n的时间索引，赋给预测序列
predict_time_index = pd.date_range(start=trend.index[-1],
periods=(args.predict_time + 1),
freq=interval)[1:]
'''
为预测出的趋势数据添加周期数据和残差数据
'''
values = []
# enumerate() 函数用于将一个可遍历的数据对象(如列表、元组或字符串)组合为一个索引序列,同时列出数据和数据下标,一般用在for循环当中。
for i, t in enumerate(predict_time_index):
trend_part = predict_data[i]
# 相同时间点的周期数据均值
# t为2018-08-09 15:18:00类型的时间,t.time()为15:18:00类型的时间
season_part = seasonal[seasonal.index.time == t.time()].mean()
# 趋势 + 周期
predict = trend_part + season_part
values.append(round(predict, 2))
# 得到预测值
final_predict = pd.Series(values, index=predict_time_index, name='predict')
return final_predict
help='Dir of the data to train')
parser.add_argument('--predict_time',
type=int,
help='The prediction time.')
args = parser.parse_args()
check_result = check_param(args)
if check_result == '':
ori_data, timestamp_list, value_list = handle_data.get_train_data(
args.data_dir, args.predict_time)
if len(value_list) < args.predict_time:
print('less original data')
else:
create_model = models.create(args.model_name,
predict_time=args.predict_time)
train_model = create_model.train(ori_data, timestamp_list,
value_list)
if train_model is not None:
predict_data = create_model.predict(train_model, value_list)
print("the prediction result:")
print(predict_data)
truth_data = handle_data.get_truth_data(
args.data_dir, args.predict_time)
if predict_data is not None and truth_data is not None:
accuracy = accuracy.pct(predict_data, truth_data)
print("the prediction error:%f" % accuracy)
#get_figure(timestamp_list, value_list, predict_data, truth_data)
else:
print('The result of prediction is null')