Triple exponential moving average¶

The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values.

Version: v1 · 2026-09-28 · History
Domain-specific #: 12636
Domain group: Social Sciences
Origin domain: Economics & Finance
Subdomain: Technical Analysis → Economics & Finance

Core Idea¶

Triple exponential moving average is treated here as the recurring cross_domain_models_structures_representations identity summarized by this source-grounded definition: The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values.

The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values. The name suggests this is achieved by applying a triple exponential smoothing which is not the case. The name triple comes from the fact that the value of an EMA (Exponential Moving Average) is triple.

To keep it in line with the actual data and to remove the lag the value "EMA of EMA" is subtracted 3 times from the previously tripled ema. Because EMA(EMA(EMA)) is used in the calculation, TEMA needs 3 × period - 2 samples to start producing values in contrast to the period samples needed by a regular EMA. Finally "EMA of EMA of EMA" is added.

For Triple exponential moving average, the abstraction is narrower than the article's general subject matter: a positive case must preserve The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values. Retaining only the name, a familiar example, or a downstream effect is insufficient. The specialist roles and tests remain anchored in cross_domain_models_structures_representations, which is why this identity is domain-specific rather than prime.

Structural Signature¶

Sig role-phrases:

Defining carrier — Because EMA(EMA(EMA)) is used in the calculation, TEMA needs 3 × period - 2 samples to start producing values in contrast to the period samples needed by a regular EMA.
Constitutive relation — The indicator was introduced in January 1994 by Patrick G.
Operating condition — The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values.
Recognition evidence — The name suggests this is achieved by applying a triple exponential smoothing which is not the case.
Admissible variation — Mulloy, in an article in the Technical Analysis of Stocks & Commodities magazine: "Smoothing Data with Faster Moving Averages" The same article also introduced another EMA related indicator: Double exponential moving average (DEMA).
Characteristic consequence — To keep it in line with the actual data and to remove the lag the value "EMA of EMA" is subtracted 3 times from the previously tripled ema.
Failure boundary — \textit{TEMA} = 3 \times \textit{EMA} - 3 \times \textit{EMA}(\textit{EMA}) + \textit{EMA}(\textit{EMA}(\textit{EMA})).

What It Is Not¶

Not the whole field of cross_domain_models_structures_representations. The node requires the specific identity stated by The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values.
Not an over-broad reading. The name suggests this is achieved by applying a triple exponential smoothing which is not the case.
Not an over-broad reading. Mulloy, in an article in the Technical Analysis of Stocks & Commodities magazine: "Smoothing Data with Faster Moving Averages" The same article also introduced another EMA related indicator: Double exponential moving average (DEMA).
Not an over-broad reading. To keep it in line with the actual data and to remove the lag the value "EMA of EMA" is subtracted 3 times from the previously tripled ema.
Not automatically Parabolic SAR. Retrieval proximity does not establish equivalence; the two identities must be compared by carrier, operation, and failure boundary.

Scope of Application¶

Triple exponential moving average applies literally inside cross_domain_models_structures_representations wherever the source-defined carrier and relation can be established. Its documented habitats include:

Formula. Because EMA(EMA(EMA)) is used in the calculation, TEMA needs 3 × period - 2 samples to start producing values in contrast to the period samples needed by a regular EMA.
History. Mulloy, in an article in the Technical Analysis of Stocks & Commodities magazine: "Smoothing Data with Faster Moving Averages" The same article also introduced another EMA related indicator: Double exponential moving average (DEMA).
Formula. To keep it in line with the actual data and to remove the lag the value "EMA of EMA" is subtracted 3 times from the previously tripled ema.
Formula. \textit{TEMA} = 3 \times \textit{EMA} - 3 \times \textit{EMA}(\textit{EMA}) + \textit{EMA}(\textit{EMA}(\textit{EMA})).
History. The indicator was introduced in January 1994 by Patrick G.
Formula. Finally "EMA of EMA of EMA" is added.

Outside cross_domain_models_structures_representations, the name should be retained only when these same operational conditions survive; otherwise the comparison belongs to the broader parent Classification or should be marked as analogy.

Clarity¶

A clear use of Triple exponential moving average names the carrier, the operative relation, and the conditions under which the source treats the identity as present. The minimal definition is The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values. The strongest recognition evidence in the frozen account is: The name suggests this is achieved by applying a triple exponential smoothing which is not the case. A report should distinguish that evidence from a proxy, consequence, or common implementation. It should also state the qualification The name suggests this is achieved by applying a triple exponential smoothing which is not the case. so that a reader can reproduce the classification rather than infer it from topical resemblance.

Manages Complexity¶

Triple exponential moving average compresses multiple cross_domain_models_structures_representations details into a stable diagnostic relation. The source shows both the central mechanism—the indicator was introduced in January 1994 by Patrick G.—and the practical consequence—to keep it in line with the actual data and to remove the lag the value "EMA of EMA" is subtracted 3 times from the previously tripled ema. This compression makes cases comparable while leaving parameters, conventions, exceptions, and evidential quality explicit. It is lossy by design: local history and implementation details may be omitted only when they do not alter the defining relation.

Abstract Reasoning¶

Type the carrier. Identify the cross_domain_models_structures_representations entities to which the claim applies.
State the relation. Use the source-grounded identity: The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values.
Check operation and conditions. The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values.
Demand recognition evidence. The name suggests this is achieved by applying a triple exponential smoothing which is not the case.
Test variation. Change an implementation or setting while preserving mulloy, in an article in the Technical Analysis of Stocks & Commodities magazine: "Smoothing Data with Faster Moving Averages" The same article also introduced another EMA related indicator: Double exponential moving average (DEMA).
Run the collapse test. Remove the defining operation; if the label still seems equally apt, only a topic or correlate was retained.
Reduce cautiously. When the specialist conditions cannot be carried, route the residual comparison to Classification.

Knowledge Transfer¶

Within the home domain. Knowledge about Triple exponential moving average transfers literally when a new case preserves the same carrier type, relation, and recognition test. Because EMA(EMA(EMA)) is used in the calculation, TEMA needs 3 × period - 2 samples to start producing values in contrast to the period samples needed by a regular EMA. Mulloy, in an article in the Technical Analysis of Stocks & Commodities magazine: "Smoothing Data with Faster Moving Averages" The same article also introduced another EMA related indicator: Double exponential moving average (DEMA).

Beyond the home domain. No canonical parent is asserted for Triple exponential moving average. An outside case receives the specialist name only when the same typed roles and rejection conditions can be filled literally; otherwise the comparison remains an analogy pending later graph densification.

Examples¶

Canonical¶

The name suggests this is achieved by applying a triple exponential smoothing which is not the case. This case is canonical because it supplies a concrete carrier and lets the defining relation be checked rather than merely named.

Mapped back: carrier → the entities in the documented case; operation → The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values; recognition evidence → The name suggests this is achieved by applying a triple exponential smoothing which is not the case

Applied / In Practice¶

Mulloy, in an article in the Technical Analysis of Stocks & Commodities magazine: "Smoothing Data with Faster Moving Averages" The same article also introduced another EMA related indicator: Double exponential moving average (DEMA). The applied case shows how the identity is used under a second setting or qualification while keeping the same operative relation.

Mapped back: changed setting → History; invariant → The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values; boundary → the case exits the class when the name suggests this is achieved by applying a triple exponential smoothing which is not the case

Structural Tensions¶

T1 — Stable identity versus admissible variation. The name suggests this is achieved by applying a triple exponential smoothing which is not the case. The tension matters because emphasizing only one side either dissolves the identity or overstates what the evidence and domain conventions warrant.

Diagnostic: Which changes preserve the defining relation, and which replace it?

T2 — Recognition versus proxy. Mulloy, in an article in the Technical Analysis of Stocks & Commodities magazine: "Smoothing Data with Faster Moving Averages" The same article also introduced another EMA related indicator: Double exponential moving average (DEMA). The tension matters because emphasizing only one side either dissolves the identity or overstates what the evidence and domain conventions warrant.

Diagnostic: Does the cited evidence establish the identity or only a correlated sign?

T3 — Definition versus implementation. To keep it in line with the actual data and to remove the lag the value "EMA of EMA" is subtracted 3 times from the previously tripled ema. The tension matters because emphasizing only one side either dissolves the identity or overstates what the evidence and domain conventions warrant.

Diagnostic: Is the observed implementation constitutive, optional, or merely common?

T4 — Scope versus overextension. \textit{TEMA} = 3 \times \textit{EMA} - 3 \times \textit{EMA}(\textit{EMA}) + \textit{EMA}(\textit{EMA}(\textit{EMA})). The tension matters because emphasizing only one side either dissolves the identity or overstates what the evidence and domain conventions warrant.

Diagnostic: Can every claimed application fill the same typed roles without metaphor?

T5 — Transfer versus domain accent. Because EMA(EMA(EMA)) is used in the calculation, TEMA needs 3 × period - 2 samples to start producing values in contrast to the period samples needed by a regular EMA. The tension matters because emphasizing only one side either dissolves the identity or overstates what the evidence and domain conventions warrant.

Diagnostic: Does the receiving case instantiate Triple exponential moving average literally, co-instantiate Classification, or only resemble it?

T6 — Autonomy versus reduction. The indicator was introduced in January 1994 by Patrick G. The tension matters because emphasizing only one side either dissolves the identity or overstates what the evidence and domain conventions warrant.

Diagnostic: What does Triple exponential moving average distinguish that the broader parent Classification leaves together?

Structural–Framed Character¶

Triple exponential moving average is mixed or framed-leaning. Its structural side is the repeatable organization summarized by The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values. Its framed side is the cross_domain_models_structures_representations vocabulary that fixes the carrier, evidence, exceptions, and admissible transformations.

Evaluative weight: the identity can be stated descriptively even when applications carry practical stakes. Human-practice dependence: the source-grounded carrier determines whether the relation exists independently or is constituted by a practice. Institutional origin: disciplinary conventions stabilize the name and test. Vocabulary portability: The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values. Import versus recognition: literal transfer requires the same mechanism; shape alone is analogy.

Its portable skeleton is Classification. Its character: a recurring specialist identity whose thin organization can be abstracted, while its operational meaning remains domain-bound.

Structural Core vs. Domain Accent¶

What is skeletal. The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values. The stable skeleton is the typed relation expressed in that definition and the entry's recognition and collapse tests. The source identifies these operative conditions: Because EMA(EMA(EMA)) is used in the calculation, TEMA needs 3 × period - 2 samples to start producing values in contrast to the period samples needed by a regular EMA. The indicator was introduced in January 1994 by Patrick G. It further constrains recognition and variation through: The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values. The name suggests this is achieved by applying a triple exponential smoothing which is not the case.

What is domain-bound. cross domain models structures representations supplies the operative entities, technical vocabulary, warrants, and exceptions that make Triple exponential moving average literal. Its documented scope includes the condition that Because EMA(EMA(EMA)) is used in the calculation, TEMA needs 3 × period - 2 samples to start producing values in contrast to the period samples needed by a regular EMA. Another bounded application condition is that Mulloy, in an article in the Technical Analysis of Stocks & Commodities magazine: "Smoothing Data with Faster Moving Averages" The same article also introduced another EMA related indicator: Double exponential moving average (DEMA). These are not decorative examples; they determine which carrier and evidence can fill the abstraction's roles.

Why no parent is asserted. Removing those specialist details does not currently yield one live catalog node that is a necessary genus for every instance. The entry is therefore approved as unparented rather than attached by topical resemblance. Its collapse evidence remains specific—Mulloy, in an article in the Technical Analysis of Stocks & Commodities magazine: "Smoothing Data with Faster Moving Averages" The same article also introduced another EMA related indicator: Double exponential moving average (DEMA).—and future graph densification may discover a defensible relation only if it preserves that boundary.

This entry is a kind of Trading Indicator.

Approved unparented node. No current live node supplies a defensible necessary genus or structural prerequisite for Triple exponential moving average. The reviewed identity is: The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values. The accelerated suggestion was declined because topical or lexical similarity does not establish hierarchy; the node is admitted without a parent pending later graph densification.
Related reasoning operations. Evidence, representation, comparison, classification, transformation, or evaluation may participate in particular cases, but participation does not make any one of them a necessary parent of every instance.

Relationships to Other Abstractions¶

Current abstraction Triple exponential moving average Domain-specific

Parents (1) — more general patterns this builds on

Triple exponential moving average is a kind of Trading Indicator Domain-specific

Triple exponential moving average satisfies the defining boundary of Trading Indicator: A trading indicator is a rule-defined transformation of time-indexed market data that produces a series, band, oscillator, threshold, or event intended to summarize trend, momentum, volatility, range, volume, or another market feature for trading analysis under specified parameters and decision conventions.

Hierarchy path (1) — routes to 1 parentless root

Triple exponential moving average → Trading Indicator → Proxy–Target Fidelity → Representation → Abstraction

Neighborhood in Abstraction Space¶

Triple exponential moving average sits in a sparse region of the domain-specific corpus (91^st percentile for distinctiveness): few abstractions share its structure, so a faithful description tends to retrieve it precisely.

Family — Financial Indices & Trading Indicators (15 abstractions)

Nearest neighbors

Value at risk — 0.80
Törnqvist index — 0.79
Single Vegetative Obstruction Model — 0.79
Relative Strength Index — 0.79
Merton's portfolio problem — 0.79

Computed from structural-signature embeddings · 2026-10-08

Not to Be Confused With¶

Classification. The parent omits the specialist differentia. Tell: Can the case establish The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight on recent values?
Parabolic SAR. A recursive technical-analysis indicator that places a trailing stop-and-reverse level behind a price trend and accelerates it toward the trend’s extreme point. Tell: Which entry's carrier, operation, and failure condition are satisfied?
Accelerated failure time model. Model covariates as multiplying an event-time scale—equivalently shifting log survival time—so coefficients are interpreted through time ratios rather than the constant hazard ratios of proportional-hazards regression. Tell: Which entry's carrier, operation, and failure condition are satisfied?
Exploratory data analysis. Exploratory data analysis denotes approach of analyzing data sets in statistics within statistics. Tell: Which entry's carrier, operation, and failure condition are satisfied?
A measurement, proxy, or consequence. Those may provide evidence without being the identity. Tell: Would Triple exponential moving average remain present if the detector or downstream effect changed?
A metaphorical analogue. A similar shape outside cross_domain_models_structures_representations lacks the specialist mechanism. Tell: Do the native roles transfer literally, or only the parent Classification?

References¶

Frozen Wikipedia discovery revision: https://en.wikipedia.org/wiki/Triple_exponential_moving_average (revision 1228740311).
Preserved source candidate: http://technical.traders.com/archive/archivelogin.asp?file=\V12\C01\SMOOTHI.pdf&src=SC
Preserved source candidate: http://technical.traders.com/archive/volume-2014.asp?yr=1994#Jan
Preserved source candidate: http://etfhq.com/blog/2010/11/17/double-and-triple-exponential-moving-average/
Preserved source candidate: https://www.tradingtechnologies.com/help/xstudy/triple-exponential-moving-average-tema/

The frozen Wikipedia revision is discovery provenance. The retained source set was reviewed for identity, formal or operational relation, and scope. The encyclopedia's structural synthesis is bounded to those claims; a thin authority surface is recorded as a nonblocking source-strengthening repair rather than concealed.