About
AutoTS is a time series forecasting package for Python that automates the creation of high-accuracy forecasts at scale. It supports dozens of modeling approaches including naive, statistical, machine learning, and deep learning methods, all usable with a familiar scikit-learn-style API on Pandas DataFrames. Key features of AutoTS: - AutoML with genetic algorithms that automatically searches for the best models, preprocessing, and ensembling for your dataset - Probabilistic forecasting with upper and lower bound predictions - Multivariate forecasting support for multiple related time series - Horizontal and mosaic ensemble methods allowing each series to receive the most accurate possible model - Over 30 time series-specific transforms with .fit(), .transform(), and .inverse_transform() methods - Scalability to hundreds of thousands of input time series - Exogenous regressor support for incorporating external variables - Cross-validation options, simulation forecasting mode, event risk forecasting, and plotting capabilities.
README
AutoTS
AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale. In 2023, AutoTS won in the M6 forecasting competition, delivering the highest performance investment decisions across 12 months of stock market forecasting. There are dozens of forecasting models usable in thesklearn style of .fit() and .predict().
These includes naive, statistical, machine learning, and deep learning models.
Additionally, there are over 30 time series specific transforms usable in the sklearn style of .fit(), .transform() and .inverse_transform().
All of these function directly on Pandas Dataframes, without the need for conversion to proprietary objects.
All models support forecasting multivariate (multiple time series) outputs and also support probabilistic (upper/lower bound) forecasts.
Most models can readily scale to tens and even hundreds of thousands of input series.
Many models also support passing in user-defined exogenous regressors.
These models are all designed for integration in an AutoML feature search which automatically finds the best models, preprocessing, and ensembling for a given dataset through genetic algorithms.
Horizontal and mosaic style ensembles are the flagship ensembling types, allowing each series to receive the most accurate possible models while still maintaining scalability.
A combination of metrics and cross-validation options, the ability to apply subsets and weighting, regressor generation tools, simulation forecasting mode, event risk forecasting, live datasets, template import and export, plotting, and a collection of data shaping parameters round out the available feature set.
Table of Contents
Installation
``
pip install autots
`
This includes dependencies for basic models, but additonal packages are required for some models and methods.
Be advised there are several other projects that have chosen similar names, so make sure you are on the right AutoTS code, papers, and documentation.
Basic Use
Input data for AutoTS is expected to come in either a *long* or a *wide* format:
The *wide* format is a pandas.DataFrame with a pandas.DatetimeIndex and each column a distinct series.
The *long* format has three columns:
- Date (ideally already in pandas-recognized datetime format)
- Series ID. For a single time series, series_id can be = None.
- Value
For *long* data, the column name for each of these is passed to .fit() as date_col, id_col, and value_col. No parameters are needed for *wide* data.
Lower-level functions are only designed for wide style data.
`python
also load: _hourly, _monthly, _weekly, _yearly, or _live_daily
from autots import AutoTS, load_daily
sample datasets can be used in either of the long or wide import shapes
long = False
df = load_daily(long=long)
model = AutoTS(
forecast_length=21,
frequency="infer",
prediction_interval=0.9,
ensemble=None,
model_list="superfast", # "fast", "default", "fast_parallel"
transformer_list="fast", # "superfast",
drop_most_recent=1,
max_generations=4,
num_validations=2,
validation_method="backwards"
)
model = model.fit(
df,
date_col='datetime' if long else None,
value_col='value' if long else None,
id_col='series_id' if long else None,
)
prediction = model.predict()
plot a sample
prediction.plot(model.df_wide_numeric,
series=model.df_wide_numeric.columns[0],
start_date="2019-01-01")
Print the details of the best model
print(model)
point forecasts dataframe
forecasts_df = prediction.forecast
upper and lower forecasts
forecasts_up, forecasts_low = prediction.upper_forecast, prediction.lower_forecast
accuracy of all tried model results
model_results = model.results()
and aggregated from cross validation
validation_results = model.results("validation")
``
The lower-level API, in particular the large section of time series transformers in the scikit-learn style, can also be utilized independently from the AutoML framework.
Check out [extended_tutRelated MCP Servers
AI Research Assistant
hamid-vakilzadeh
AI Research Assistant provides comprehensive access to millions of academic papers through the Semantic Scholar and arXiv databases. This MCP server enables AI coding assistants to perform intelligent literature searches, citation network analysis, and paper content extraction without requiring an API key. Key features include: - Advanced paper search with multi-filter support by year ranges, citation thresholds, field of study, and publication type - Title matching with confidence scoring for finding specific papers - Batch operations supporting up to 500 papers per request - Citation analysis and network exploration for understanding research relationships - Full-text PDF extraction from arXiv and Wiley open-access content (Wiley TDM token required for institutional access) - Rate limits of 100 requests per 5 minutes with options to request higher limits through Semantic Scholar
Linkup
LinkupPlatform
Linkup is a real-time web search and content extraction service that enables AI assistants to search the web and retrieve information from trusted sources. It provides source-backed answers with citations, making it ideal for fact-checking, news gathering, and research tasks. Key features of Linkup: - Real-time web search using natural language queries to find current information, news, and data - Page fetching to extract and read content from any webpage URL - Search depth modes: Standard for direct-answer queries and Deep for complex research across multiple sources - Source-backed results with citations and context from relevant, trustworthy websites - JavaScript rendering support for accessing dynamic content on JavaScript-heavy pages
Math-MCP
EthanHenrickson
Math-MCP is a computation server that enables Large Language Models (LLMs) to perform accurate numerical calculations through the Model Context Protocol. It provides precise mathematical operations via a simple API to overcome LLM limitations in arithmetic and statistical reasoning. Key features of Math-MCP: - Basic arithmetic operations: addition, subtraction, multiplication, division, modulo, and bulk summation - Statistical analysis functions: mean, median, mode, minimum, and maximum calculations - Rounding utilities: floor, ceiling, and nearest integer rounding - Trigonometric functions: sine, cosine, tangent, and their inverses with degrees and radians conversion support