monash_tsf /
kashif's picture
initial monash time series forecasting repository (#3743)
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(
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.")
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]))
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.")
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 == "?":
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")
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.")
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 (
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:
if len(parts) == 2:
return convert_multiple(parts[0]) + BASE_FREQ_TO_PANDAS_OFFSET[parts[1]]
raise ValueError(f"Invalid frequency string {freq}.")
"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)