auto-regressive integrated moving average (arima) indicator and trading performance.

ARIMA Indicator Defination.

Auto-Regressive Integrated Moving Average (ARIMA) indicator ek statistical tool hai jo time-series data ke patterns ko analyze karne ke liye use kiya jaata hai. ARIMA indicator mein time-series data ke past values aur trends ko analyze karke future predictions kiye jaate hain.



ARIMA Indicator Trend prediction.

ARIMA indicator forex trading mein trend analysis ke liye use kiya jaata hai. Iske through traders price movements aur trend directions ko analyze kar sakte hain aur future price movements ke predictions kar sakte hain.

ARIMA Indicator Parameters.

ARIMA indicator ke liye kuch important parameters hote hain:

1. P (Auto-regressive parameter): Is parameter se past values ko analyze kiya jaata hai.

2. D (Integrated parameter): Is parameter se data ko stationary banane ke liye analyze kiya jaata hai.



3. Q (Moving average parameter): Is parameter se errors ko analyze kiya jaata hai.

ARIMA Indicator Benefits.

ARIMA indicator forex trading mein bahut useful hai. Iske kuch benefits hain:

1. Trend analysis: ARIMA indicator ke through traders price movements aur trend directions ko analyze kar sakte hain.

2. Future predictions: Iske use se traders future price movements ke predictions kar sakte hain.

3. Data analysis: ARIMA indicator ke use se data ko analyze karke traders apne trading strategies ko improve kar sakte hain.

ARIMA Indicator Performance.

ARIMA indicator ka performance forex trading mein bahut accha hai. Iske use se traders ko bahut saari information milti hai jaise ki trend analysis, future predictions aur data analysis.



Is ke use se traders apne trading strategies ko improve kar sakte hain aur profitable trades kar sakte hain.

Points of learnings.

Auto-Regressive Integrated Moving Average (ARIMA) indicator forex trading mein bahut useful hai. Iske use se traders trend analysis aur future predictions kar sakte hain aur apne trading strategies ko improve kar sakte hain. Iske use se traders profitable trades kar sakte hain.

Auto-Regressive Integrated Moving Average (ARIMA) Indicator aur Trading Performance

• Introduction:
• ARIMA, jo ke "Auto-Regressive Integrated Moving Average" ko mukhtasar karta hai, ek aham technical analysis tool hai jo traders ke liye mahatvapurn hai.
  
  ARIMA, financial markets mein price movements ko analyze karne aur forecast karne ke liye istemal kiya jane wala ek popular statistical method hai. Iska mukhya uddeshya past data ke patterns aur trends ko samajh kar future ke price movements ko predict karna hai. Traders aur analysts ARIMA ka istemal karke market trends ko samajhte hain aur trading decisions ko lenge ke liye taiyar hote hain.

• ARIMA ka Tareeqa:
• ARIMA indicator, past data ke basis par future ke patterns ko predict karta hai.
  
  ARIMA ka tareeqa, time series data analysis par mabni hota hai. Is tareeqe mein, past data ke patterns ko dekhte hue future ke price movements ko forecast kiya jata hai. ARIMA, stationary aur non-stationary time series data ko analyze karne ke liye istemal kiya ja sakta hai aur iske parameters ko set karne ke liye past data ke trends ka dhyan rakha jata hai.

Components of ARIMA:

ARIMA teen mukhya components par mabni hota hai: Auto-Regressive (AR), Integrated (I), aur Moving Average (MA).

Auto-Regressive (AR):

Is component mein, future values past ke values par depend karte hain. Yani ke, current value ko past values se predict kiya jata hai.

Integrated (I):

Integrated component data ko stationary banata hai taki time series analysis ke liye sahih ho. Isse data ke trend ko analyze karne mein madad milti hai.

Moving Average (MA):

MA component past errors ko use karta hai future trends ko predict karne ke liye. Yeh component past ki errors ko smooth karne mein madad deta hai.

• Auto-Regressive (AR):
• ARIMA ke is component mein, future values past ke values par depend karte hain.
  
  Auto-Regressive (AR) component, past data ke patterns aur trends ko analyze karke future ke price movements ko predict karta hai. Is component mein, current value ko past values se explain kiya jata hai, jisse future ke price movements ko samajhne mein madad milti hai.
  
  ARIMA mein, AR component ka parameter "p" hota hai, jo past data ke kitne time periods ko consider kiya jaye, ko darust karta hai. Is parameter ko set karne mein, traders ko past data ke trends aur patterns ka dhyan rakhna hota hai.

Integrated (I):

Integrated component data ko stationary banata hai taki time series analysis ke liye sahih ho.

Integrated (I) component, time series data ko stationary banane mein madad deta hai. Stationary data, jisme mean aur variance constant rehte hain, ko analyze karna ARIMA ke liye zaroori hota hai.

I component ko "d" parameter se represent kiya jata hai, jo data ko kitni dafa difference kiya gaya hai stationary banane ke liye, ko darust karta hai. Is parameter ko set karne mein, data ke volatility aur trends ka dhyan rakha jata hai.

• Moving Average (MA):
• MA component past errors ko use karta hai future trends ko predict karne ke liye.
  
  Moving Average (MA) component, past errors ko analyze karke future ke trends ko predict karta hai. Is component mein, past ki errors ko smooth karke future ke price movements ko forecast kiya jata hai.
  
  MA component ko "q" parameter se represent kiya jata hai, jo past errors ko kitne time periods tak consider kiya jaye, ko darust karta hai. Is parameter ko set karne mein, traders ko past ki errors aur volatility ka dhyan rakhna hota hai.

• ARIMA Parameters:
• ARIMA ke parameters ko set karne mein, pichle data ke trends aur patterns ko dhyan mein rakha jata hai.
  
  ARIMA ke parameters ko set karne mein, traders ko past data ke trends aur patterns ka dhyan rakhna hota hai. Saheeh parameters ke tay karna, accurate predictions aur trading strategies develop karne ke liye zaroori hota hai.
  
  ARIMA ke parameters, AR (p), I (d), aur MA (q) hote hain, jo past data ke analysis ke adhar par tay kiye jate hain. In parameters ko sahih tareeke se set karne ke liye, traders ko past data ke patterns ko samajhna aur future ke price movements ko anticipate karna hota hai.

Application in Trading:

ARIMA indicator ko trading mein istemal karke, traders future price movements ko anticipate kar sakte hain.

ARIMA indicator, trading mein mahatvapurn role ada karta hai. Iska istemal karke, traders future price movements ko analyze karke trading decisions ko lenge ke liye taiyar hote hain. ARIMA ke predictions aur signals ke madad se, traders market trends ko samajh kar profitable trading opportunities ko identify kar sakte hain.

ARIMA ka istemal karne se, traders market volatility aur risks ko bhi samajh sakte hain, jisse unki trading performance improve hoti hai. Is tareeqe ka istemal karke, traders apni trading strategies ko refine kar sakte hain aur consistent profits earn kar sakte hain.


Forecasting Trends:

ARIMA ki madad se traders future price trends ko analyze kar sakte hain aur trading strategies ko taiyar kar sakte hain.

ARIMA ki madad se, traders future price trends ko analyze karke trading strategies ko taiyar kar sakte hain. Is indicator ke predictions aur signals ke madad se, traders market ke trends ko samajh sakte hain aur future price movements ko anticipate kar sakte hain.

ARIMA ke istemal se, traders market volatility aur risks ko bhi samajh sakte hain, jisse unki trading decisions sahih aur successful hoti hai. Is tareeqe ka istemal karke, traders apni trading performance ko improve kar sakte hain aur consistent profits earn kar sakte hain.


Risk Management:

ARIMA ki sahih istemal se traders apni risk management strategies ko improve kar sakte hain.

ARIMA ke predictions aur signals ke madad se, traders apni risk management strategies ko improve kar sakte hain. Is indicator ki madad se, traders market volatility aur risks ko samajh sakte hain aur apni trading decisions ko sahih tareeke se manage kar sakte hain.

ARIMA ke istemal se, traders apni risk exposure ko kam kar sakte hain aur apni positions ko hedging karne ke liye tayari kar sakte hain. Is tareeqe se, unki trading performance mein consistency aati hai aur losses ko minimize kiya ja sakta hai.

Backtesting:

ARIMA ke istemal se traders apni trading strategies ko backtest kar sakte hain, jisse unhein pichli performance ka pata chalta hai.

ARIMA ke predictions aur signals ko past data par apply karke, traders apni trading strategies ko backtest kar sakte hain. Is tareeqe se, unhein pichli performance ka pata chalta hai aur wo apni strategies ko refine kar sakte hain. Backtesting ke through, traders apni strategies ki effectiveness aur reliability ko test kar sakte hain, jisse future mein better trading decisions liye ja sakein.


Volatility Analysis:

ARIMA indicator ki madad se traders market ki volatility ko analyze kar sakte hain aur is par amal karne ke tareeqe tay kar sakte hain.

Market volatility, trading mein ek important factor hai jo traders ke liye challenges create karta hai. ARIMA ke istemal se, traders market ki volatility ko analyze kar sakte hain aur is par amal karne ke tareeqe tay kar sakte hain. Is tareeqe se, unhein market ke fluctuations ko samajhne aur unke sath deal karne ke liye tayari milti hai. Volatility analysis ke through, traders apni positions ko adjust kar sakte hain aur market ke changing conditions mein bhi successful trading kar sakte hain.

• Time Series Forecasting:
• ARIMA, time series data ke analysis mein madadgar hai aur future price movements ko predict karne mein asani deta hai.
  
  Time series data analysis, trading mein ek crucial aspect hai jo future price movements ko predict karne mein madad karta hai. ARIMA, time series data ke analysis mein madadgar hai aur future price movements ko predict karne mein asani deta hai. Is tareeqe se, traders market trends ko samajh sakte hain aur profitable trading decisions le sakte hain. Time series forecasting ke through, traders market ke direction ko samajh kar apni trading strategies ko optimize kar sakte hain.

Trading Performance:

ARIMA ke sahih istemal se traders apni trading performance ko improve kar sakte hain aur consistent profits earn kar sakte hain.

ARIMA ke sahih istemal se, traders apni trading performance ko improve kar sakte hain aur consistent profits earn kar sakte hain. Is indicator ki madad se, traders market trends ko samajh sakte hain aur accurate predictions karke trading decisions le sakte hain. Saheeh analysis aur risk management ke saath, traders apni trading performance ko enhance kar sakte hain aur market mein successful ho sakte hain.


Conclusion:

ARIMA indicator ek powerful tool hai jo traders ko market trends ko samajhne aur trading strategies ko improve karne mein madad deta hai, agar sahih tareeke se istemal kiya jaye.

In conclusion, ARIMA ek powerful tool hai jo traders ko market trends ko analyze karne aur future price movements ko predict karne mein madad deta hai. Iska sahih istemal karke, traders apni trading performance ko improve kar sakte hain aur consistent profits earn kar sakte hain. However, ARIMA ke sahih tareeke se istemal karne ke liye, traders ko past data ke patterns aur trends ko samajhna aur sahih parameters ko set karna zaroori hai. Overall, ARIMA trading mein ek valuable tool hai jo traders ko market ka insight deta hai aur unhein successful trading ke liye tayyar karta hai.

Auto-Regressive Integrated Moving Average (ARIMA) Indicator aur Forex Trading Mein Trading Ka Kaamyaabi

Sar-e-Maqala: Forex trading mein kamyabi hasil karne ke liye, traders ko mukhtalif technical indicators ka istemal karna hota hai. Ek aham aur mashhoor indicator jo trading mein istemal kiya jata hai, woh hai Auto-Regressive Integrated Moving Average (ARIMA). Is article mein hum dekheinge ke ARIMA indicator kya hai aur kaise iska istemal Forex trading mein hota hai.

1. ARIMA Indicator ka Tareekhi Peshgoi: ARIMA, yaani Auto-Regressive Integrated Moving Average, ek statistical model hai jo time-series data ko analyze karne ke liye istemal hota hai. Iska tareekhi pehchaan 1970s mein kiya gaya tha aur tab se hi iska istemal mukhtalif fields mein hota raha hai, jaise ke finance aur economics. ARIMA model ke development ne financial markets mein trading ko mazeed samajhne aur predict karne mein madad ki.

2. ARIMA Indicator ka Maqsad: ARIMA ka maqsad time-series data ke patterns aur trends ko samajhna hai taake future values predict kiya ja sake. Is indicator ke zariye traders currency pairs ke future movements ko forecast karte hain. Iska maqsad traders ko market ke future direction ka andaza lagane mein madad karna hai.

3. ARIMA ka Tareeqa-e-Kar: ARIMA indicator apne tareeqa-e-kar mein mukhtalif qisam ke parameters istemal karta hai, jinmein shamil hain autoregression (AR), integration (I), aur moving average (MA). Yeh parameters time-series data ke patterns ko samajhne aur future values ko predict karne mein madadgar hotay hain. ARIMA ke tareeqa-e-kar mein, pehle data ko stationarity ka test kia jata hai, phir model ke parameters ko set kiya jata hai, aur akhir mein future values ko forecast kiya jata hai.

4. ARIMA ka Istemal Forex Trading Mein: Forex trading mein ARIMA indicator ka istemal currency pairs ke future movements ko forecast karne ke liye hota hai. Traders is indicator ke zariye market ke trends aur patterns ko samajhte hain aur apne trading strategies ko uske mutabiq tay karte hain. Is indicator ka istemal kar ke traders currency pairs ke direction ka andaza lagate hain aur iske mutabiq trading decisions lete hain.

5. ARIMA Indicator ki Takneek: ARIMA indicator ki takneek mein sab se pehle time-series data ko analyze kiya jata hai. Phir is data ko stationarity ka test kiya jata hai taake ARIMA model ko sahi tareeqe se apply kiya ja sake. Stationarity ka test isliye important hai kyunki stationary data ke absence mein ARIMA model sahi tareeqe se kaam nahi kar sakta. Iske baad, ARIMA model ke parameters ko estimate kiya jata hai aur model ko fit kiya jata hai. Akhir mein, is model ka istemal kar ke future values ko forecast kiya jata hai.

6. ARIMA Model ke Parameters: ARIMA model ke parameters mein sab se ahem hain autoregression (AR) parameter, integration (I) parameter, aur moving average (MA) parameter. In parameters ko sahi tareeqe se set karne se accurate predictions hasil ki ja sakti hain. ARIMA model ke parameters ko estimate karne ke liye mukhtalif techniques istemal kiye jate hain, jaise ke maximum likelihood estimation.

7. ARIMA Indicator ka Istemal ke Fawaid: ARIMA indicator ka istemal kar ke traders currency pairs ke future movements ko samajh sakte hain aur iske mutabiq trading decisions le sakte hain. Is tarah, unhe market mein kamyabi hasil karne mein madad milti hai. ARIMA indicator ke istemal se traders ko market ke trends aur patterns ka behtar understanding hota hai, jo unhe trading ke liye faida pohochata hai.

8. ARIMA ke Istemal ka Tareeqa: ARIMA indicator ka istemal karne ka tareeqa samajhna aur isko sahi tareeqe se interpret karna aham hai. Traders ko is indicator ki readings ko analyze kar ke apne trading strategies ko adjust karna chahiye. Is indicator ko sahi tareeqe se istemal karne ke liye, traders ko market ke mukhtalif factors ko madde nazar rakhte hue apne decisions ko lena chahiye. Iske alawa, traders ko ARIMA model ke limitations ko bhi samajhna zaroori hai, jaise ke stationary data requirement aur short-term forecasts ki accuracy.

9. ARIMA Indicator ki Challenges: ARIMA indicator ke istemal mein kuch challenges bhi hote hain, jaise ke time-series data ke fluctuations aur unpredictable market conditions. Isliye, traders ko is indicator ko sahi tareeqe se istemal karne ke liye tayyar rehna chahiye. Stationarity ka hona, data ke sahi tareeqe se clean hona, aur model ke parameters ka sahi tareeqe se estimate karna, in challenges ko handle karne mein madadgar ho sakti hai.

10. ARIMA aur Technical Analysis: ARIMA indicator ek aham hissa hai technical analysis ka jo traders ko market trends aur patterns ko samajhne mein madad deta hai. Iske istemal se traders apni trading strategies ko improve kar sakte hain. Technical analysis ke saath-saath, ARIMA indicator ki readings ko interpret kar ke traders market ke future direction ka andaza lagate hain aur iske mutabiq trading decisions lete hain.

11. ARIMA ka Istemal kaamyaabi ke Liye: ARIMA indicator ka istemal karne ke liye traders ko sahi tareeqe se data ko analyze karna aur iske parameters ko set karna aham hai. Iske saath hi, traders ko market ke mukhtalif factors ko bhi madde nazar rakhte hue apne decisions ko lena chahiye. Iske alawa, risk management ka bhi khayal rakhna zaroori hai taake trading losses ko minimize kiya ja sake aur trading performance ko improve kiya ja sake.

12. ARIMA aur Risk Management: ARIMA indicator ke istemal ke saath-saath, traders ko risk management ka bhi khayal rakhna zaroori hai. Ismein
amil hai stop-loss orders ka istemal aur position sizes ka tay karna taake trading losses ko minimize kiya ja sake. Risk management ke tareeqay traders ko trading mein consistent performance maintain karne mein madadgar hote hain aur unhe trading ke challenges ka samna karne mein madad dete hain.

13. ARIMA ka Mawazna aur Performance: ARIMA indicator ke performance ko measure karne ke liye traders ko mukhtalif time periods par iska mawazna karna chahiye. Iske zariye unhe indicator ki accuracy aur reliability ka andaza ho sakta hai. Performance ko evaluate karte waqt, traders ko model ke predictions ko actual market movements ke saath mawazna karna chahiye. Iske alawa, ARIMA model ke parameters ko optimize karne ke liye mukhtalif techniques ka istemal kiya ja sakta hai taake model ki performance improve ki ja sake.

14. ARIMA ka Future: ARIMA indicator ki demand aur istemal mein mazeed izafa hone ka imkan hai, khaaskar Forex trading mein. Iska istemal kar ke traders apne trading strategies ko aur bhi behtar bana sakte hain aur market mein kamyabi hasil kar sakte hain. Future mein, ARIMA model ke further development aur improvements ki zaroorat hai taake iska istemal trading mein aur bhi effective ho sake. Iske saath-saath, machine learning aur artificial intelligence techniques ka istemal bhi ARIMA model ke enhancement ke liye kiya ja sakta hai.

Ikhtitami Khyalat: ARIMA indicator ek aham tool hai Forex trading mein jo traders ko market ke trends aur patterns ko samajhne mein madad deta hai. Iske sahi tareeqe se istemal karne se traders apne trading performance ko improve kar sakte hain aur kamyabi hasil kar sakte hain. Iske alawa, traders ko ARIMA model ke limitations aur challenges ko samajhna zaroori hai taake iska istemal karne mein asani ho. Overall, ARIMA indicator trading mein ek powerful tool hai jo traders ko market analysis aur forecasting mein madad deta hai.

AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)

ARIMA aik statistical model hai jo time series data ko analyze karne aur future values ko predict karne ke liye use hota hai. Ye model teen main components ka istemal karta hai: Auto-Regressive (AR), Integrated (I), aur Moving Average (MA).

AUTO-REGRESSIVE (AR)

Auto-Regressive component ka matlab hai ke current value ko past values ki linear combination se predict kiya jata hai. Ismein ek parameter "p" hota hai jo indicate karta hai ke kitni past values ko consider kiya gaya hai.

INTEGRATED (I)

Integrated component ka matlab hai data ko stationarity ke liye difference karna. Stationarity se murad hai ke time series ka mean aur variance time ke sath change nahi hota. Parameter "d" indicate karta hai ke kitni dafa difference liya gaya hai.

PROCESS
Agar data stationary nahi hai, toh hum first difference lete hain:

MOVING AVERAGE (MA)

Moving Average component ka matlab hai ke current value ko past error terms (residuals) ki linear combination se predict kiya jata hai. Ismein ek parameter "q" hota hai jo indicate karta hai ke kitni past errors ko consider kiya gaya hai. ARIMA model teen parameters ka combination hota hai:
p-Auto-Regressive terms ki number
d-Differencing ki number for stationarity q-Moving Average terms ki number

USES AND ANALYSIS

ARIMA model ka istemal forecasting aur trend analysis ke liye hota hai. Yeh financial markets, economics, aur various scientific fields mein bahut useful hota hai. ARIMA flexible hai aur different patterns aur trends ko effectively capture kar sakta hai. ARIMA aik powerful tool hai time series forecasting ke liye. Ye model apni simplicity aur flexibility ki wajah se widely use hota hai aur accurate predictions provide karne mein madadgar hai.

ARIMA Indicator aur Trading Performance

Muqadma: ARIMA (Auto-Regressive Integrated Moving Average) ek statistical analysis method hai jo time series data ko analyze karne aur predict karne ke liye istimal hoti hai. Yeh indicator trading mein bhi kafi maqbool hai, kyunke yeh future price movements ko accurately forecast karne ki salahiyat rakhta hai. ARIMA model ko trading mein effective tareeqe se use karna kaafi fruitful ho sakta hai agar isse sahi tariqe se samjha aur implement kiya jaye.

ARIMA Model Ki Samajh: ARIMA model ko teen components mein divide kiya jata hai:

1. Auto-Regressive (AR): Yeh component pichli values ko istimal karta hai future values ko predict karne ke liye.
2. Integrated (I): Yeh component data ko stationary banane ke liye differencing ka istimal karta hai, taake data ke mean aur variance waqt ke saath change na kare.
3. Moving Average (MA): Yeh component past forecast errors ko istimal karta hai future values ko predict karne ke liye.

Model Ki Identification: ARIMA model ko identify karne ke liye p, d, aur q parameters set karte hain:

• p (AR order): Kitni past values ko consider karna hai.
• d (Differencing order): Kitni baar data ko stationary banane ke liye differencing karna hai.
• q (MA order): Kitni past forecast errors ko consider karna hai.

ARIMA Model Ka Estimation: Ek martaba p, d, aur q parameters ko select kar lete hain, uske baad model ko data par fit karte hain. Estimation process mein, model ko training data par apply karte hain aur parameters ko optimize karte hain taake best fit mil sake.

Model Diagnostic: Estimation ke baad, diagnostic checks kiye jate hain taake ensure ho ke model sahi kaam kar raha hai. Yeh checks include karte hain:

• Residual Analysis: Residuals ko check karna taake ensure ho ke woh white noise hain (i.e., unmein koi pattern nahi hai).
• ACF/PACF Plots: Autocorrelation aur partial autocorrelation plots ko check karna taake koi bhi significant lags identify kiye ja sakein.

Forecasting Aur Trading: Model diagnostic ke baad, ARIMA model ko future price movements predict karne ke liye use kiya jata hai. Trading ke liye yeh predictions kaafi valuable hote hain, kyunke yeh trading signals generate karte hain:

• Buy Signal: Agar forecasted price current price se zyada hai, to buy signal generate hota hai.
• Sell Signal: Agar forecasted price current price se kam hai, to sell signal generate hota hai.

Trading Performance: ARIMA model ka trading performance evaluate karne ke liye kuch key metrics ko dekha jata hai:

• Accuracy: Kitne predictions sahi hain.
• Profitability: Kitna profit generate hua hai.
• Risk Management: Kitna risk manage kiya gaya hai, aur drawdown kitna kam hua hai.

Khatma: ARIMA indicator trading mein kaafi effective ho sakta hai agar isse sahi tariqe se use kiya jaye. Yeh model price movements ko accurately forecast karne ki salahiyat rakhta hai, jo ke trading signals generate karne mein madadgar hoti hai. Lekin, jaise har trading strategy ke saath hota hai, risk management aur proper analysis ki zaroorat hoti hai taake consistent results mil sakein. ARIMA model ko achi tarah samajh kar aur implement kar ke trading performance ko enhance kiya ja sakta hai.

Forex Mein Auto-Regressive Integrated Moving Average (ARIMA) Indicator"!"!"!"!

ARIMA (Auto-Regressive Integrated Moving Average) Forex trading mein aik mathematical model hai jo time series data ko analyze aur forecast karne ke liye use hota hai.



Forex Mein Auto-Regressive Integrated Moving Average (ARIMA) Indicator Ke Components"!"!"!"!

ARIMA model ke components ko detail mein samajhte hain. Ye model teen main components par mabni hota hai: Auto-Regressive (AR), Integrated (I), aur Moving Average (MA).
1. Auto-Regressive (AR) Component

• Explanation: Ye component pichle waqt ke values ka use karke current value ko predict karta hai.
• Example: Agar AR(1) model hai, to aapka current value (yt) pichle value (yt-1) par mabni hoga.
• Formula: 𝑦𝑡=𝑐+𝜙𝑦𝑡−1+𝜖𝑡yt=c+ϕyt−1+ϵt
  • y: current value
  • c: constant term
  • \phi: coefficient of lagged value
  • \epsilon_t: error term (white noise)

2. Integrated (I) Component

• Explanation: Iska kam data ko stationary banana hota hai. Stationary ka matlab hai ke data ka mean aur variance time ke sath constant rahen.
• Example: Differencing technique use karke hum trend aur seasonality ko hata sakte hain. Pehle order differencing mein 𝑦𝑡yt aur 𝑦𝑡−1yt−1 ka difference lete hain.
• Formula: 𝑦𝑡′=𝑦𝑡−𝑦𝑡−1yt′=yt−yt−1

3. Moving Average (MA) Component

• Explanation: Ye component error terms ka use karke current value ko predict karta hai.
• Example: Agar MA(1) model hai, to current value (yt) pichle error term (et-1) par mabni hoga.
• Formula: 𝑦𝑡=𝑐+𝜖𝑡+𝜃𝜖𝑡−1yt=c+ϵt+θϵt−1
  • y: current value
  • c: constant term
  • \epsilon_t: current error term
  • \theta: coefficient of lagged error term

Combined ARIMA Model

• Explanation: ARIMA model in teeno components ko combine karta hai.
• Example: ARIMA(p,d,q) model mein:
  • p: Auto-Regressive (AR) terms ki count
  • d: Differencing (I) ki count to make series stationary
  • q: Moving Average (MA) terms ki count
• Formula: 𝑦𝑡=𝑐+𝜙1𝑦𝑡−1+𝜙2𝑦𝑡−2+...+𝜙𝑝𝑦𝑡−𝑝+𝜃1𝜖𝑡−1+𝜃2𝜖𝑡−2+...+𝜃𝑞 𝜖𝑡−𝑞+𝜖𝑡yt=c+ϕ1yt−1+ϕ2yt−2+...+ϕpyt−p+θ1ϵt−1+θ 2ϵt−2+...+θqϵt−q+ϵt

Is tarah se ARIMA model Forex trading mein currency rates ke future trends ko predict karne mein madadgar hota hai.