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These are some key concepts related to time series forecasting,
designed to help you better understand and leverage the capabilities
of TimeGPT.
Time Series
A time series is a sequence of data points indexed by time, used to
model phenomena that changes over time, such as stock prices,
temperature, or product sales. A time series can generally be thought of
as comprising the following components:
-
Trend: The consistent, long-term direction of the data, whether
upward or downward. It reflects the persistent, overall movement in
the series over time.
-
Seasonality: A repeated cycle around a known and fixed period.
-
Remainder: The residuals or random noise left in the data after
the trend and seasonal effects have been accounted for.
Forecasting
Forecasting is the process of predicting the future values of a time
series based on historical data. It plays a crucial role in the
decision-making process across various fields such as finance,
healthcare, retail, and economics, among others.
Forecasting can use a variety of approaches, from statistical approaches
to novel techniques such as machine learning, deep learning, and
foundation models. These models can be further classified into
univariate and multivariate models, depending on the number of variables
used to make the predictions, or local or global models, with local
models estimating parameters independently for each series and global
models estimating parameters jointly across multiple series.
Forecasts themselves can be presented as point forecasts, which predict
a single future value, or as probabilistic forecasts, which provide a
full probability distribution of future values, and hence, providing a
measure of uncertainty.
Foundation Model
Foundation model refers to a type of large, pre-trained model that can
be adapted to a wide range of tasks, including time series forecasting.
Originally developed for domains such as natural language processing and
computer vision, foundation models are now increasingly applied to
sequential data like time series. These models are typically trained on
extensive datasets, capturing complex patterns and dependencies that can
be fine-tuned for specific tasks.
TimeGPT
Developed by Nixtla, TimeGPT is the first foundation model for time
series forecasting. TimeGPT was trained on billions of observations
from publicly available datasets across multiple domains and can produce
accurate forecasts for new time series without additional training,
using only historical values as inputs. The model ‘reads’ time series
data similarly to how humans read a sentence—sequentially from left to
right. It looks at windows of past data, which we can think of as
‘tokens’, and predicts what comes next. This prediction is based on
patterns the model identifies in past data and extrapolates into the
future.
Tokens
TimeGPT processes time series data in chunks. Each data point in a
series can be thought of as a ‘token’, akin to how individual words or
characters are treated in natural language processing (NLP).
Fine-tuning
Fine-tuning is a process used in machine learning where a pre-trained
model like TimeGPT undergoes additional training to adapt it for a
specific dataset. Initially, TimeGPT can operate in a zero-shot
manner, meaning it can generate forecasts as-is. While this zero-shot
approach provides a solid baseline, the performance of TimeGPT can
often be improved through fine-tuning. During this process, the
TimeGPT model undergoes additional training using the specific
dataset, starting from the pre-trained parameters. The updated model
then produces the forecasts.
Learn how to fine-tune
TimeGPT
Historical Forecasts
Historical forecasts, also known as in-sample forecasts, are the
predictions made for the historical data. These forecasts are commonly
used to evaluate the performance of forecasting models by comparing the
predicted values against the actual values.
Learn how to make historical forecasts with
TimeGPT
Anomaly Detection
Anomaly detection refers to the process of identifying unusual
observations that deviate significantly from the expected behavior of
the data. Anomalies, also known as outliers, can be caused by a variety
of factors, such as errors in the data collection process, sudden
changes in the underlying patterns of the data, or unexpected events.
These anomalies can pose challenges for many forecasting models, as they
may distort trends, seasonal patterns, or estimates of autocorrelation.
Consequently, anomalies can significantly impact the accuracy of
forecasts. Therefore, it is crucial to be able to identify them
accurately.
Anomaly detection has many applications across different industries,
including detecting fraud in financial transactions, monitoring the
performance of online services, or identifying unusual patterns in
energy usage.
Learn how to detect anomalies with
TimeGPT
Time Series Cross Validation
Time series cross-validation is a method for evaluating how a model
would have performed on historical data. It works by defining a sliding
window across past observations and predicting the period following it.
It differs from standard cross-validation by maintaining the
chronological order of the data instead of randomly splitting it.
This method allows for a more accurate estimation of a forecasting
model’s predictive capabilities by considering multiple sequential
periods. When only one window is used, this method resembles a standard
train-test split, with the last set of observations serving as the test
data and all preceding data as the training set.
Learn how to perform cross-validation with
TimeGPT
Exogenous Variables
Exogenous variables are external factors that can influence the behavior
of a time series but are not directly affected by it. For example, in
retail sales forecasting, exogenous variables could include factors such
as holidays, promotions, prices, or weather data for electricity load
forecasts. By incorporating these variables into the forecasting model,
it is possible to capture the relationships between the target series
and external factors, leading to more accurate predictions.
Learn how to include exogenous variables in
TimeGPT 
