Question on time series/cauchy distribution

In summary: So in summary, we can write Form 2 as Y_t = C cos(λt + φ), where C = √(B_1^2 + B_2^2) and φ = arctan(B_2/B_1). Therefore, Form 2 can be written in a manner similar to Form 1.
  • #1
caspian2012
4
0
1. (Form 1) X_t = A cos(λt +φ ) ,
where φ was Unif [−pi ,pi ] , λ is a fixed constant and A is a constant (or a RV mean 0,
variance \σ^\2_\A, and indep of φ ). ]
Now consider the following: Let B_1 , B_2 be IID Normal(0,\σ^\2_\B) and λ a fixed constant
(Form 2) Y_t = B_1 cos(λ_t) + B2 sin(λ_t)
Can Form 2 be written in a manner similar to Form 1? If so, show how.
[Hint: you know the distribution of {\frac{\-B_2}{B_1}}
(Cauchy). Define
φ = arctan({\frac{\-B_2}{B_1}}) . What is the distribution of φ ?]


I am lost in what I need to do. Could someone give me some help? Or some hint?

Thanks!
 
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  • #2
This is my first post so I guess I will try to type everything again. Hopefully it works this time.
 
  • #3
1. (Form 1) [tex]X_t [/tex]= A cos(λt +φ ) ,
where φ was Unif [−pi ,pi ] , λ is a fixed constant and A is a constant (or a RV mean 0,
variance [tex]\σ^\2_\A[/tex], and indep of φ ). ]
Now consider the following: Let [tex]B_1[/tex] , [tex]B_2 [/tex]be IID Normal(0,[tex]\σ^\2_\B[/tex]) and λ a fixed constant
(Form 2) [tex]Y_t[/tex] = [tex]B_1[/tex] cos([tex]λ_t[/tex]) + B2 sin([tex]λ_t[/tex])
Can Form 2 be written in a manner similar to Form 1? If so, show how.
[Hint: you know the distribution of [tex]{\frac{\-B_2}{B_1}}[/tex]
(Cauchy). Define
φ = arctan([tex]{\frac{\-B_2}{B_1}}[/tex]) . What is the distribution of φ ?]
 
  • #4
caspian2012 said:
1. (Form 1) X_t = A cos(λt +φ ) ,
where φ was Unif [−pi ,pi ] , λ is a fixed constant and A is a constant (or a RV mean 0,
variance \σ^\2_\A, and indep of φ ). ]
Now consider the following: Let B_1 , B_2 be IID Normal(0,\σ^\2_\B) and λ a fixed constant
(Form 2) Y_t = B_1 cos(λ_t) + B2 sin(λ_t)
Can Form 2 be written in a manner similar to Form 1? If so, show how.
[Hint: you know the distribution of {\frac{\-B_2}{B_1}}
(Cauchy). Define
φ = arctan({\frac{\-B_2}{B_1}}) . What is the distribution of φ ?]


I am lost in what I need to do. Could someone give me some help? Or some hint?

Thanks!

Just use standard trigonometric identities; in particular, how can you write sin(a+b) in terms of sin(a), sin(b), cos(a) and cos(b)?

RGV
 
  • #5
Ray Vickson said:
Just use standard trigonometric identities; in particular, how can you write sin(a+b) in terms of sin(a), sin(b), cos(a) and cos(b)?

RGV

Thanks. I thought it would be much more tricky, guess not.
 

1. What is a time series?

A time series is a sequence of data points collected at regular intervals over a period of time. It is used to analyze trends, patterns, and changes in the data over time.

2. What is a Cauchy distribution?

A Cauchy distribution is a probability distribution that describes the distribution of a continuous random variable. It is characterized by its location parameter (median) and scale parameter (half-width at half-maximum).

3. How is a Cauchy distribution different from a normal distribution?

A Cauchy distribution has a heavier tail compared to a normal distribution, which means it has more extreme values. The Cauchy distribution also does not have a defined mean and variance like the normal distribution.

4. What are some applications of time series analysis?

Time series analysis is used in various fields, such as economics, finance, weather forecasting, and signal processing. It is especially useful in predicting future trends and making informed decisions based on past patterns.

5. How can time series be used to forecast future data?

There are several techniques used in time series analysis to forecast future data, including exponential smoothing, autoregression, and moving average. These methods use past data to identify patterns and trends, which are then used to make predictions about future data points.

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