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Time Series Forecasting
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from datetime import datetime | |
from distutils.util import strtobool | |
import numpy as np | |
import pandas as pd | |
# Converts the contents in a .tsf file into a dataframe and returns | |
# it along with other meta-data of the dataset: | |
# frequency, horizon, whether the dataset contains missing values and whether the series have equal lengths | |
# | |
# Parameters | |
# full_file_path_and_name - complete .tsf file path | |
# replace_missing_vals_with - a term to indicate the missing values in series in the returning dataframe | |
# value_column_name - Any name that is preferred to have as the name of the column containing series values in the returning dataframe | |
def convert_tsf_to_dataframe( | |
full_file_path_and_name, | |
replace_missing_vals_with="NaN", | |
value_column_name="series_value", | |
): | |
col_names = [] | |
col_types = [] | |
all_data = {} | |
line_count = 0 | |
frequency = None | |
forecast_horizon = None | |
contain_missing_values = None | |
contain_equal_length = None | |
found_data_tag = False | |
found_data_section = False | |
started_reading_data_section = False | |
with open(full_file_path_and_name, "r", encoding="cp1252") as file: | |
for line in file: | |
# Strip white space from start/end of line | |
line = line.strip() | |
if line: | |
if line.startswith("@"): # Read meta-data | |
if not line.startswith("@data"): | |
line_content = line.split(" ") | |
if line.startswith("@attribute"): | |
if len(line_content) != 3: # Attributes have both name and type | |
raise ValueError("Invalid meta-data specification.") | |
col_names.append(line_content[1]) | |
col_types.append(line_content[2]) | |
else: | |
if len(line_content) != 2: # Other meta-data have only values | |
raise ValueError("Invalid meta-data specification.") | |
if line.startswith("@frequency"): | |
frequency = line_content[1] | |
elif line.startswith("@horizon"): | |
forecast_horizon = int(line_content[1]) | |
elif line.startswith("@missing"): | |
contain_missing_values = bool(strtobool(line_content[1])) | |
elif line.startswith("@equallength"): | |
contain_equal_length = bool(strtobool(line_content[1])) | |
else: | |
if len(col_names) == 0: | |
raise ValueError("Missing attribute section. Attribute section must come before data.") | |
found_data_tag = True | |
elif not line.startswith("#"): | |
if len(col_names) == 0: | |
raise ValueError("Missing attribute section. Attribute section must come before data.") | |
elif not found_data_tag: | |
raise ValueError("Missing @data tag.") | |
else: | |
if not started_reading_data_section: | |
started_reading_data_section = True | |
found_data_section = True | |
all_series = [] | |
for col in col_names: | |
all_data[col] = [] | |
full_info = line.split(":") | |
if len(full_info) != (len(col_names) + 1): | |
raise ValueError("Missing attributes/values in series.") | |
series = full_info[len(full_info) - 1] | |
series = series.split(",") | |
if len(series) == 0: | |
raise ValueError( | |
"A given series should contains a set of comma separated numeric values. At least one numeric value should be there in a series. Missing values should be indicated with ? symbol" | |
) | |
numeric_series = [] | |
for val in series: | |
if val == "?": | |
numeric_series.append(replace_missing_vals_with) | |
else: | |
numeric_series.append(float(val)) | |
if numeric_series.count(replace_missing_vals_with) == len(numeric_series): | |
raise ValueError( | |
"All series values are missing. A given series should contains a set of comma separated numeric values. At least one numeric value should be there in a series." | |
) | |
all_series.append(np.array(numeric_series, dtype=np.float32)) | |
for i in range(len(col_names)): | |
att_val = None | |
if col_types[i] == "numeric": | |
att_val = int(full_info[i]) | |
elif col_types[i] == "string": | |
att_val = str(full_info[i]) | |
elif col_types[i] == "date": | |
att_val = datetime.strptime(full_info[i], "%Y-%m-%d %H-%M-%S") | |
else: | |
raise ValueError( | |
"Invalid attribute type." | |
) # Currently, the code supports only numeric, string and date types. Extend this as required. | |
if att_val is None: | |
raise ValueError("Invalid attribute value.") | |
else: | |
all_data[col_names[i]].append(att_val) | |
line_count = line_count + 1 | |
if line_count == 0: | |
raise ValueError("Empty file.") | |
if len(col_names) == 0: | |
raise ValueError("Missing attribute section.") | |
if not found_data_section: | |
raise ValueError("Missing series information under data section.") | |
all_data[value_column_name] = all_series | |
loaded_data = pd.DataFrame(all_data) | |
return ( | |
loaded_data, | |
frequency, | |
forecast_horizon, | |
contain_missing_values, | |
contain_equal_length, | |
) | |
def convert_multiple(text: str) -> str: | |
if text.isnumeric(): | |
return text | |
if text == "half": | |
return "0.5" | |
def frequency_converter(freq: str): | |
parts = freq.split("_") | |
if len(parts) == 1: | |
return BASE_FREQ_TO_PANDAS_OFFSET[parts[0]] | |
if len(parts) == 2: | |
return convert_multiple(parts[0]) + BASE_FREQ_TO_PANDAS_OFFSET[parts[1]] | |
raise ValueError(f"Invalid frequency string {freq}.") | |
BASE_FREQ_TO_PANDAS_OFFSET = { | |
"seconds": "S", | |
"minutely": "T", | |
"minutes": "T", | |
"hourly": "H", | |
"hours": "H", | |
"daily": "D", | |
"days": "D", | |
"weekly": "W", | |
"weeks": "W", | |
"monthly": "M", | |
"months": "M", | |
"quarterly": "Q", | |
"quarters": "Q", | |
"yearly": "Y", | |
"years": "Y", | |
} | |
# Example of usage | |
# loaded_data, frequency, forecast_horizon, contain_missing_values, contain_equal_length = convert_tsf_to_dataframe("TSForecasting/tsf_data/sample.tsf") | |
# print(loaded_data) | |
# print(frequency) | |
# print(forecast_horizon) | |
# print(contain_missing_values) | |
# print(contain_equal_length) | |