Summary

Frame statistics

Number of series	13
Time series length	129
Start date	2012-01
End date	2022-09
Frequency	MS
Period	12
Total size in memory	14.4 kB
Average series size	1.1 kB

Frame details

Description	Beef Incidents
Source	not provided

Time Series

Incidents	Incidents
Above Limit Lab Tests	Above Limit Lab Tests
Trade (tonnes)	Trade (tonnes)
Production (tonnes)	Production (tonnes)
Media Buzz	Media Buzz
EU Price	EU Price
Risk
Disease Cases
Disease Deaths	Disease Deaths
Price VS Mean	Price VS Mean
Production VS Mean	Production VS Mean
Trade VS Mean	Trade VS Mean
Risk Normalized	Risk Normalized

Execution stats

Started	2022-12-14 17:47:27.286076
Ended	2022-12-14 17:47:31.258179
Duration	0:00:03.972103
Package	`tslumen==0.0.1`

Configuration details

{'acf': {'adjusted': False,
         'alpha': 0.05,
         'fft': False,
         'lags': 40,
         'missing': 'none'},
 'acf_1d': {'adjusted': False,
            'alpha': 0.05,
            'fft': False,
            'lags': 40,
            'missing': 'none'},
 'acf_2d': {'adjusted': False,
            'alpha': 0.05,
            'fft': False,
            'lags': 40,
            'missing': 'none'},
 'adfuller_stationarity': {'confidence_level': 0.05},
 'binned': {'nbins': None},
 'corr_kendall': {},
 'corr_pearson': {},
 'corr_spearman': {},
 'cov': {},
 'df_scaled': {},
 'dt_end': {},
 'dt_start': {},
 'freq': {},
 'ft_acf': {'n_diff': (0, 1, 2), 'n_size': (1, 10)},
 'ft_adfuller': {},
 'ft_cross_pts': {},
 'ft_entropy': {'n_per_segment': None, 'sampling_frequency': 1.0},
 'ft_kpss': {},
 'ft_pacf': {'n_diff': (0, 1, 2), 'n_size': (5,)},
 'ft_stl': {'freq': None},
 'ft_tilewin': {},
 'granger_causality': {'addconst': True,
                       'adf_confidence': 0.1,
                       'max_diff': 3,
                       'maxlag': 5,
                       'test': 'ssr_chi2test'},
 'infinite': {},
 'iqr': {},
 'jarque_bera_normality': {'confidence_level': 0.05},
 'kpss_stationarity': {'confidence_level': 0.05},
 'kurtosis': {},
 'lag_corr': {'lags': ()},
 'length': {},
 'levene_constant_variance': {'confidence_level': 0.05},
 'ljungbox_autocorrelation': {'confidence_level': 0.05, 'n_lags': None},
 'lowess': {'delta': 0.0,
            'fracs': (0.05, 0.1, 0.15),
            'it': 3,
            'missing': 'drop'},
 'mad': {},
 'maximum': {},
 'mean': {},
 'median': {},
 'minimum': {},
 'missing': {},
 'n_series': {},
 'omnibus_normality': {'confidence_level': 0.05},
 'pacf': {'alpha': 0.05, 'lags': 40, 'method': 'ywadjusted'},
 'pacf_1d': {'alpha': 0.05, 'lags': 40, 'method': 'ywadjusted'},
 'pacf_2d': {'alpha': 0.05, 'lags': 40, 'method': 'ywadjusted'},
 'pd_percentiles': {},
 'pd_quantiles': {},
 'period': {},
 'q25': {},
 'q50': {},
 'q75': {},
 'rolling_avg': {'max_win_frac': 10, 'wins': ()},
 'sample': {'sample_size': 10},
 'seasonal_split': {},
 'skew': {},
 'std': {},
 'stl': {'low_pass': None,
         'low_pass_deg': 0,
         'low_pass_jump': 1,
         'period': None,
         'robust': False,
         'seasonal': 7,
         'seasonal_deg': 0,
         'seasonal_jump': 1,
         'trend': None,
         'trend_deg': 0,
         'trend_jump': 1},
 'supsmu': {'alpha': None,
            'final_span': 0.05,
            'middle_span': 0.2,
            'period': None,
            'primary_spans': (0.05, 0.2, 0.5)},
 'sz_total': {'memory_deep': True},
 'var': {},
 'zeros': {}}

acf:
  adjusted: false
  alpha: 0.05
  fft: false
  lags: 40
  missing: none
acf_1d:
  adjusted: false
  alpha: 0.05
  fft: false
  lags: 40
  missing: none
acf_2d:
  adjusted: false
  alpha: 0.05
  fft: false
  lags: 40
  missing: none
adfuller_stationarity:
  confidence_level: 0.05
binned:
  nbins: null
corr_kendall: {}
corr_pearson: {}
corr_spearman: {}
cov: {}
df_scaled: {}
dt_end: {}
dt_start: {}
freq: {}
ft_acf:
  n_diff: !!python/tuple
  - 0
  - 1
  - 2
  n_size: !!python/tuple
  - 1
  - 10
ft_adfuller: {}
ft_cross_pts: {}
ft_entropy:
  n_per_segment: null
  sampling_frequency: 1.0
ft_kpss: {}
ft_pacf:
  n_diff: !!python/tuple
  - 0
  - 1
  - 2
  n_size: !!python/tuple
  - 5
ft_stl:
  freq: null
ft_tilewin: {}
granger_causality:
  addconst: true
  adf_confidence: 0.1
  max_diff: 3
  maxlag: 5
  test: ssr_chi2test
infinite: {}
iqr: {}
jarque_bera_normality:
  confidence_level: 0.05
kpss_stationarity:
  confidence_level: 0.05
kurtosis: {}
lag_corr:
  lags: !!python/tuple []
length: {}
levene_constant_variance:
  confidence_level: 0.05
ljungbox_autocorrelation:
  confidence_level: 0.05
  n_lags: null
lowess:
  delta: 0.0
  fracs: !!python/tuple
  - 0.05
  - 0.1
  - 0.15
  it: 3
  missing: drop
mad: {}
maximum: {}
mean: {}
median: {}
minimum: {}
missing: {}
n_series: {}
omnibus_normality:
  confidence_level: 0.05
pacf:
  alpha: 0.05
  lags: 40
  method: ywadjusted
pacf_1d:
  alpha: 0.05
  lags: 40
  method: ywadjusted
pacf_2d:
  alpha: 0.05
  lags: 40
  method: ywadjusted
pd_percentiles: {}
pd_quantiles: {}
period: {}
q25: {}
q50: {}
q75: {}
rolling_avg:
  max_win_frac: 10
  wins: !!python/tuple []
sample:
  sample_size: 10
seasonal_split: {}
skew: {}
std: {}
stl:
  low_pass: null
  low_pass_deg: 0
  low_pass_jump: 1
  period: null
  robust: false
  seasonal: 7
  seasonal_deg: 0
  seasonal_jump: 1
  trend: null
  trend_deg: 0
  trend_jump: 1
supsmu:
  alpha: null
  final_span: 0.05
  middle_span: 0.2
  period: null
  primary_spans: !!python/tuple
  - 0.05
  - 0.2
  - 0.5
sz_total:
  memory_deep: true
var: {}
zeros: {}

df_scaled
dt_end
dt_start
freq
infinite
length
missing
n_series
period
sample
- sample_size: 10
sz_total
- memory_deep: True
zeros
cov
iqr
kurtosis
mad
maximum
mean
median
minimum
q25
q50
q75
skew
std
var
adfuller_stationarity
- confidence_level: 0.05
jarque_bera_normality
- confidence_level: 0.05
kpss_stationarity
- confidence_level: 0.05
levene_constant_variance
- confidence_level: 0.05
ljungbox_autocorrelation
- n_lags: None
- confidence_level: 0.05
omnibus_normality
- confidence_level: 0.05
binned
- nbins: None
pd_percentiles
pd_quantiles
acf
- lags: 40
- adjusted: False
- fft: False
- alpha: 0.05
- missing: none
acf_1d
- lags: 40
- adjusted: False
- fft: False
- alpha: 0.05
- missing: none
acf_2d
- lags: 40
- adjusted: False
- fft: False
- alpha: 0.05
- missing: none
corr_kendall
corr_pearson
corr_spearman
granger_causality
- test: ssr_chi2test
- addconst: True
- maxlag: 5
- max_diff: 3
- adf_confidence: 0.1
lag_corr
- lags: ()
pacf
- lags: 40
- method: ywadjusted
- alpha: 0.05
pacf_1d
- lags: 40
- method: ywadjusted
- alpha: 0.05
pacf_2d
- lags: 40
- method: ywadjusted
- alpha: 0.05
seasonal_split
stl
- period: None
- seasonal: 7
- trend: None
- low_pass: None
- seasonal_deg: 0
- trend_deg: 0
- low_pass_deg: 0
- robust: False
- seasonal_jump: 1
- trend_jump: 1
- low_pass_jump: 1
lowess
- fracs: (0.05, 0.1, 0.15)
- it: 3
- delta: 0.0
- missing: drop
rolling_avg
- wins: ()
- max_win_frac: 10
supsmu
- alpha: None
- period: None
- primary_spans: (0.05, 0.2, 0.5)
- middle_span: 0.2
- final_span: 0.05
ft_acf
- n_diff: (0, 1, 2)
- n_size: (1, 10)
ft_adfuller
ft_cross_pts
ft_entropy
- sampling_frequency: 1.0
- n_per_segment: None
ft_kpss
ft_pacf
- n_diff: (0, 1, 2)
- n_size: (5,)
ft_stl
- freq: None
ft_tilewin

Time Series

Incidents

Series details

Mean	12.91
Deviation	18.90
Maximum	142.00
Minimum	1.00
Zeros	0
Missing	0
Infinites	0

Sample

2012-01	2012-02	2012-03	2012-04	2012-05	...	2022-05	2022-06	2022-07	2022-08	2022-09
4.00	6.00	5.00	2.00	11.00	...	4.00	3.00	11.00	10.00	9.00

Statistics

	Value
Mean	12.91
Variance	357.11
Standard deviation	18.90
Median	8.00
Median absolute deviation	3.00
Coefficient of variation	1.46
Minimum	1.00
25%	5.00
50%	8.00
75%	12.00
Maximum	142.00
Interquartile range	7.00
Kurtosis	24.97
Skewness	4.70

Statistical Tests

Test	Null Hypothesis	Reject	pvalue	Confidence
Levene	Variance is constant between 10 groups	No	0.064	0.05
Ljung-Box	No autocorrelation among specified lag(22)	Yes	0.000	0.05
Jarque Bera	Data is normally distributed	Yes	0.000	0.05
Omnibus	Data is normally distributed	Yes	0.000	0.05
Augmented Dickey-Fuller	Data has unit root	No	0.232	0.05
Kwiatkowski-Phillips-Schmidt-Shin	Data is level stationary	No	0.083	0.05

Distribution

Main Features

trend	0.325
seasonality	0.169
entropy	0.878
entropy_acf	0.702
instability	0.226
lumpiness	2.53

Stationarity

kpss(c)	0.433
kpss(ct)	0.184
adfuller(c)	0.232
adfuller(ct)	0.534
adfuller(ctt)	0.651
adfuller(nc)	0.209

ACF/PACF

acf1(d=0)	0.122
acf1(d=1)	-0.618
acf1(d=2)	-0.760
acf10(d=0)	0.455
acf10(d=1)	0.955
acf10(d=2)	1.34
pacf5(d=0)	0.234
pacf5(d=1)	0.637
pacf5(d=2)	1.12
acf1(error)	-0.195
acf10(error)	0.295
acf(season)	0.145
pacf(season)	-0.149

Time Series Decomposition

Auto Correlation

Lag Plots

Seasonality plots

Smoothing

Above Limit Lab Tests

Series details

Mean	5.98
Deviation	44.41
Maximum	505.00
Minimum	0
Zeros	94
Missing	0
Infinites	0

Sample

2012-01	2012-02	2012-03	2012-04	2012-05	...	2022-05	2022-06	2022-07	2022-08	2022-09
0	0	0	0	0	...	0	0	0	0	0

Statistics

	Value
Mean	5.98
Variance	1.97k
Standard deviation	44.41
Median	0
Median absolute deviation	0
Coefficient of variation	7.42
Minimum	0
25%	0
50%	0
75%	1.00
Maximum	505.00
Interquartile range	1.00
Kurtosis	120.62
Skewness	11.00

Statistical Tests

Test	Null Hypothesis	Reject	pvalue	Confidence
Levene	Variance is constant between 10 groups	No	0.274	0.05
Ljung-Box	No autocorrelation among specified lag(22)	No	0.806	0.05
Jarque Bera	Data is normally distributed	Yes	0.000	0.05
Omnibus	Data is normally distributed	Yes	0.000	0.05
Augmented Dickey-Fuller	Data has unit root	Yes	0.000	0.05
Kwiatkowski-Phillips-Schmidt-Shin	Data is level stationary	No	0.100	0.05

Distribution

Main Features

trend	0.142
seasonality	0.103
entropy	0.996
entropy_acf	0.566
instability	0.150
lumpiness	10.24

Stationarity

kpss(c)	0.113
kpss(ct)	0.112
adfuller(c)	0
adfuller(ct)	0
adfuller(ctt)	0
adfuller(nc)	0

ACF/PACF

acf1(d=0)	0.00955
acf1(d=1)	-0.524
acf1(d=2)	-0.686
acf10(d=0)	0.00555
acf10(d=1)	0.278
acf10(d=2)	0.518
pacf5(d=0)	0.00398
pacf5(d=1)	0.493
pacf5(d=2)	1.06
acf1(error)	-0.136
acf10(error)	0.0573
acf(season)	-0.0121
pacf(season)	-0.0135

Time Series Decomposition

Auto Correlation

Lag Plots

Seasonality plots

Smoothing

Trade (tonnes)

Series details

Mean	489.69k
Deviation	10.47k
Maximum	498.82k
Minimum	468.50k
Zeros	0
Missing	0
Infinites	0

Sample

2012-01	2012-02	2012-03	2012-04	2012-05	...	2022-05	2022-06	2022-07	2022-08	2022-09
489.99k	489.99k	489.99k	489.99k	489.99k	...	498.82k	498.82k	498.82k	498.82k	498.82k

Statistics

	Value
Mean	489.69k
Variance	109.53m
Standard deviation	10.47k
Median	493.89k
Median absolute deviation	4.93k
Coefficient of variation	0.0214
Minimum	468.50k
25%	481.21k
50%	493.89k
75%	498.82k
Maximum	498.82k
Interquartile range	17.61k
Kurtosis	-0.708
Skewness	-0.818

Statistical Tests

Test	Null Hypothesis	Reject	pvalue	Confidence
Levene	Variance is constant between 10 groups	No	nan	0.05
Ljung-Box	No autocorrelation among specified lag(22)	Yes	0.000	0.05
Jarque Bera	Data is normally distributed	Yes	0.000	0.05
Omnibus	Data is normally distributed	Yes	0.000	0.05
Augmented Dickey-Fuller	Data has unit root	No	0.350	0.05
Kwiatkowski-Phillips-Schmidt-Shin	Data is level stationary	Yes	0.010	0.05

Distribution

Main Features

trend	0.877
seasonality	0.0274
entropy	0.443
entropy_acf	0.271
instability	1.12
lumpiness	0

Stationarity

kpss(c)	1.28
kpss(ct)	0.144
adfuller(c)	0.350
adfuller(ct)	0.230
adfuller(ctt)	0.513
adfuller(nc)	0.740

ACF/PACF

acf1(d=0)	0.938
acf1(d=1)	-0.000368
acf1(d=2)	-0.500
acf10(d=0)	4.66
acf10(d=1)	0.00000145
acf10(d=2)	0.250
pacf5(d=0)	0.884
pacf5(d=1)	0.00000070
pacf5(d=2)	0.491
acf1(error)	0.575
acf10(error)	0.556
acf(season)	0.256
pacf(season)	-0.0471

Time Series Decomposition

Auto Correlation

Lag Plots

Seasonality plots

Smoothing

Production (tonnes)

Series details

Mean	308.09m
Deviation	9.88m
Maximum	319.40m
Minimum	293.74m
Zeros	0
Missing	0
Infinites	0

Sample

2012-01	2012-02	2012-03	2012-04	2012-05	...	2022-05	2022-06	2022-07	2022-08	2022-09
293.74m	293.74m	293.74m	293.74m	293.74m	...	319.40m	319.40m	319.40m	319.40m	319.40m

Statistics

	Value
Mean	308.09m
Variance	97.65t
Standard deviation	9.88m
Median	307.51m
Median absolute deviation	9.71m
Coefficient of variation	0.0321
Minimum	293.74m
25%	298.51m
50%	307.51m
75%	319.40m
Maximum	319.40m
Interquartile range	20.89m
Kurtosis	-1.70
Skewness	0.00454

Statistical Tests

Test	Null Hypothesis	Reject	pvalue	Confidence
Levene	Variance is constant between 10 groups	No	nan	0.05
Ljung-Box	No autocorrelation among specified lag(22)	Yes	0.000	0.05
Jarque Bera	Data is normally distributed	Yes	0.000	0.05
Omnibus	Data is normally distributed	Yes	0.000	0.05
Augmented Dickey-Fuller	Data has unit root	No	0.722	0.05
Kwiatkowski-Phillips-Schmidt-Shin	Data is level stationary	Yes	0.010	0.05

Distribution

Main Features

trend	0.994
seasonality	0.480
entropy	0.200
entropy_acf	0.109
instability	1.07
lumpiness	0

Stationarity

kpss(c)	2.55
kpss(ct)	0.271
adfuller(c)	0.722
adfuller(ct)	0.295
adfuller(ctt)	0.623
adfuller(nc)	0.856

ACF/PACF

acf1(d=0)	0.981
acf1(d=1)	-0.0364
acf1(d=2)	-0.500
acf10(d=0)	8.04
acf10(d=1)	0.0142
acf10(d=2)	0.250
pacf5(d=0)	0.963
pacf5(d=1)	0.00805
pacf5(d=2)	0.491
acf1(error)	0.545
acf10(error)	0.832
acf(season)	0.771
pacf(season)	-0.0107

Time Series Decomposition

Auto Correlation

Lag Plots

Seasonality plots

Smoothing

Media Buzz

Series details

Mean	13.67
Deviation	7.10
Maximum	34.00
Minimum	2.00
Zeros	0
Missing	0
Infinites	0

Sample

2012-01	2012-02	2012-03	2012-04	2012-05	...	2022-05	2022-06	2022-07	2022-08	2022-09
14.00	8.00	11.00	22.00	23.00	...	17.00	29.00	14.00	20.00	6.00

Statistics

	Value
Mean	13.67
Variance	50.39
Standard deviation	7.10
Median	13.00
Median absolute deviation	5.00
Coefficient of variation	0.519
Minimum	2.00
25%	8.00
50%	13.00
75%	18.00
Maximum	34.00
Interquartile range	10.00
Kurtosis	-0.296
Skewness	0.594

Statistical Tests

Test	Null Hypothesis	Reject	pvalue	Confidence
Levene	Variance is constant between 10 groups	Yes	0.001	0.05
Ljung-Box	No autocorrelation among specified lag(22)	Yes	0.000	0.05
Jarque Bera	Data is normally distributed	Yes	0.018	0.05
Omnibus	Data is normally distributed	Yes	0.022	0.05
Augmented Dickey-Fuller	Data has unit root	Yes	0.003	0.05
Kwiatkowski-Phillips-Schmidt-Shin	Data is level stationary	Yes	0.010	0.05

Distribution

Main Features

trend	0.612
seasonality	0.339
entropy	0.821
entropy_acf	0.563
instability	0.398
lumpiness	0.229

Stationarity

kpss(c)	0.933
kpss(ct)	0.263
adfuller(c)	0.00344
adfuller(ct)	0.00474
adfuller(ctt)	0.00228
adfuller(nc)	0.250

ACF/PACF

acf1(d=0)	0.568
acf1(d=1)	-0.442
acf1(d=2)	-0.682
acf10(d=0)	1.38
acf10(d=1)	0.334
acf10(d=2)	0.671
pacf5(d=0)	0.463
pacf5(d=1)	0.273
pacf5(d=2)	0.813
acf1(error)	0.0115
acf10(error)	0.124
acf(season)	0.320
pacf(season)	0.155

Time Series Decomposition

Auto Correlation

Lag Plots

Seasonality plots

Smoothing

EU Price

Series details

Mean	374.76
Deviation	11.93
Maximum	398.93
Minimum	347.91
Zeros	0
Missing	0
Infinites	0

Sample

2012-01	2012-02	2012-03	2012-04	2012-05	...	2022-05	2022-06	2022-07	2022-08	2022-09
382.72	383.99	384.36	381.14	380.79	...	368.62	368.62	368.62	368.62	368.62

Statistics

	Value
Mean	374.76
Variance	142.35
Standard deviation	11.93
Median	373.59
Median absolute deviation	8.02
Coefficient of variation	0.0318
Minimum	347.91
25%	368.62
50%	373.59
75%	383.97
Maximum	398.93
Interquartile range	15.35
Kurtosis	-0.542
Skewness	-0.0664

Statistical Tests

Test	Null Hypothesis	Reject	pvalue	Confidence
Levene	Variance is constant between 10 groups	Yes	0.005	0.05
Ljung-Box	No autocorrelation among specified lag(22)	Yes	0.000	0.05
Jarque Bera	Data is normally distributed	No	0.434	0.05
Omnibus	Data is normally distributed	No	0.319	0.05
Augmented Dickey-Fuller	Data has unit root	No	0.239	0.05
Kwiatkowski-Phillips-Schmidt-Shin	Data is level stationary	Yes	0.010	0.05

Distribution

Main Features

trend	0.907
seasonality	0.552
entropy	0.562
entropy_acf	0.322
instability	0.841
lumpiness	0.0464

Stationarity

kpss(c)	1.42
kpss(ct)	0.0798
adfuller(c)	0.239
adfuller(ct)	0.0669
adfuller(ctt)	0.0935
adfuller(nc)	0.566

ACF/PACF

acf1(d=0)	0.955
acf1(d=1)	0.497
acf1(d=2)	-0.189
acf10(d=0)	3.95
acf10(d=1)	0.619
acf10(d=2)	0.171
pacf5(d=0)	1.21
pacf5(d=1)	0.341
pacf5(d=2)	0.163
acf1(error)	0.777
acf10(error)	1.16
acf(season)	0.383
pacf(season)	-0.141

Time Series Decomposition

Auto Correlation

Lag Plots

Seasonality plots

Smoothing

Risk

Series details

Mean	297.71
Deviation	320.74
Maximum	1.70k
Minimum	25.20
Zeros	0
Missing	0
Infinites	0

Sample

2012-01	2012-02	2012-03	2012-04	2012-05	...	2022-05	2022-06	2022-07	2022-08	2022-09
282.50	290.10	292.30	273.20	271.10	...	79.00	79.00	79.00	79.00	79.00

Statistics

	Value
Mean	297.71
Variance	102.88k
Standard deviation	320.74
Median	201.30
Median absolute deviation	122.30
Coefficient of variation	1.08
Minimum	25.20
25%	79.00
50%	201.30
75%	346.10
Maximum	1.70k
Interquartile range	267.10
Kurtosis	4.51
Skewness	2.15

Statistical Tests

Test	Null Hypothesis	Reject	pvalue	Confidence
Levene	Variance is constant between 10 groups	Yes	0.000	0.05
Ljung-Box	No autocorrelation among specified lag(22)	Yes	0.000	0.05
Jarque Bera	Data is normally distributed	Yes	0.000	0.05
Omnibus	Data is normally distributed	Yes	0.000	0.05
Augmented Dickey-Fuller	Data has unit root	No	0.059	0.05
Kwiatkowski-Phillips-Schmidt-Shin	Data is level stationary	No	0.100	0.05

Distribution

Main Features

trend	0.734
seasonality	0.119
entropy	0.726
entropy_acf	0.231
instability	0.893
lumpiness	0.219

Stationarity

kpss(c)	0.264
kpss(ct)	0.262
adfuller(c)	0.0595
adfuller(ct)	0.196
adfuller(ctt)	0.220
adfuller(nc)	0.0385

ACF/PACF

acf1(d=0)	0.770
acf1(d=1)	-0.358
acf1(d=2)	-0.640
acf10(d=0)	2.64
acf10(d=1)	0.133
acf10(d=2)	0.445
pacf5(d=0)	0.671
pacf5(d=1)	0.151
pacf5(d=2)	0.796
acf1(error)	0.212
acf10(error)	0.110
acf(season)	0.175
pacf(season)	0.101

Time Series Decomposition

Auto Correlation

Lag Plots

Seasonality plots

Smoothing

Disease Cases

Series details

Mean	524.67k
Deviation	336.74k
Maximum	1.05m
Minimum	1.05k
Zeros	0
Missing	0
Infinites	0

Sample

2012-01	2012-02	2012-03	2012-04	2012-05	...	2022-05	2022-06	2022-07	2022-08	2022-09
1,012.76k	1,012.76k	1,012.76k	1,012.76k	1,012.76k	...	3.18k	3.18k	3.18k	3.18k	3.18k

Statistics

	Value
Mean	524.67k
Variance	113.40b
Standard deviation	336.74k
Median	646.44k
Median absolute deviation	116.50k
Coefficient of variation	0.642
Minimum	1.05k
25%	16.07k
50%	646.44k
75%	747.25k
Maximum	1.05m
Interquartile range	731.18k
Kurtosis	-0.990
Skewness	-0.531

Statistical Tests

Test	Null Hypothesis	Reject	pvalue	Confidence
Levene	Variance is constant between 10 groups	Yes	0.000	0.05
Ljung-Box	No autocorrelation among specified lag(22)	Yes	0.000	0.05
Jarque Bera	Data is normally distributed	Yes	0.003	0.05
Omnibus	Data is normally distributed	Yes	0.000	0.05
Augmented Dickey-Fuller	Data has unit root	No	0.771	0.05
Kwiatkowski-Phillips-Schmidt-Shin	Data is level stationary	Yes	0.010	0.05

Distribution

Main Features

trend	0.967
seasonality	0.0864
entropy	0.381
entropy_acf	0.177
instability	0.942
lumpiness	0.00161

Stationarity

kpss(c)	2.10
kpss(ct)	0.372
adfuller(c)	0.771
adfuller(ct)	0.566
adfuller(ctt)	0.632
adfuller(nc)	0.0762

ACF/PACF

acf1(d=0)	0.966
acf1(d=1)	-0.0169
acf1(d=2)	-0.500
acf10(d=0)	6.71
acf10(d=1)	0.00642
acf10(d=2)	0.252
pacf5(d=0)	0.935
pacf5(d=1)	0.00159
pacf5(d=2)	0.481
acf1(error)	0.592
acf10(error)	0.862
acf(season)	0.593
pacf(season)	-0.0209

Time Series Decomposition

Auto Correlation

Lag Plots

Seasonality plots

Smoothing

Disease Deaths

Series details

Mean	12.51k
Deviation	8.29k
Maximum	28.32k
Minimum	29.00
Zeros	0
Missing	0
Infinites	0

Sample

2012-01	2012-02	2012-03	2012-04	2012-05	...	2022-05	2022-06	2022-07	2022-08	2022-09
21.80k	21.80k	21.80k	21.80k	21.80k	...	1.41k	1.41k	1.41k	1.41k	1.41k

Statistics

	Value
Mean	12.51k
Variance	68.68m
Standard deviation	8.29k
Median	12.36k
Median absolute deviation	5.11k
Coefficient of variation	0.662
Minimum	29.00
25%	1.41k
50%	12.36k
75%	17.46k
Maximum	28.32k
Interquartile range	16.06k
Kurtosis	-1.01
Skewness	-0.0692

Statistical Tests

Test	Null Hypothesis	Reject	pvalue	Confidence
Levene	Variance is constant between 10 groups	Yes	0.000	0.05
Ljung-Box	No autocorrelation among specified lag(22)	Yes	0.000	0.05
Jarque Bera	Data is normally distributed	No	0.061	0.05
Omnibus	Data is normally distributed	Yes	0.000	0.05
Augmented Dickey-Fuller	Data has unit root	No	0.477	0.05
Kwiatkowski-Phillips-Schmidt-Shin	Data is level stationary	Yes	0.010	0.05

Distribution

Main Features

trend	0.906
seasonality	0.0759
entropy	0.669
entropy_acf	0.167
instability	0.914
lumpiness	0.0324

Stationarity

kpss(c)	1.76
kpss(ct)	0.174
adfuller(c)	0.477
adfuller(ct)	0.225
adfuller(ctt)	0.391
adfuller(nc)	0.126

ACF/PACF

acf1(d=0)	0.944
acf1(d=1)	-0.00421
acf1(d=2)	-0.500
acf10(d=0)	5.33
acf10(d=1)	0.0594
acf10(d=2)	0.336
pacf5(d=0)	0.895
pacf5(d=1)	0.0000928
pacf5(d=2)	0.465
acf1(error)	0.598
acf10(error)	0.824
acf(season)	0.485
pacf(season)	-0.0233

Time Series Decomposition

Auto Correlation

Lag Plots

Seasonality plots

Smoothing

Price VS Mean

Series details

Mean	4.82
Deviation	3.92
Maximum	18.55
Minimum	0
Zeros	0
Missing	0
Infinites	0

Sample

2012-01	2012-02	2012-03	2012-04	2012-05	...	2022-05	2022-06	2022-07	2022-08	2022-09
5.10	3.83	3.46	6.68	7.03	...	0.00	0.00	0.00	0.00	0.00

Statistics

	Value
Mean	4.82
Variance	15.33
Standard deviation	3.92
Median	4.50
Median absolute deviation	2.55
Coefficient of variation	0.812
Minimum	0
25%	1.54
50%	4.50
75%	6.55
Maximum	18.55
Interquartile range	5.01
Kurtosis	0.764
Skewness	0.885

Statistical Tests

Test	Null Hypothesis	Reject	pvalue	Confidence
Levene	Variance is constant between 10 groups	Yes	0.011	0.05
Ljung-Box	No autocorrelation among specified lag(22)	Yes	0.000	0.05
Jarque Bera	Data is normally distributed	Yes	0.000	0.05
Omnibus	Data is normally distributed	Yes	0.000	0.05
Augmented Dickey-Fuller	Data has unit root	No	0.113	0.05
Kwiatkowski-Phillips-Schmidt-Shin	Data is level stationary	Yes	0.022	0.05

Distribution

Main Features

trend	0.447
seasonality	0.228
entropy	0.882
entropy_acf	0.621
instability	0.352
lumpiness	0.242

Stationarity

kpss(c)	0.610
kpss(ct)	0.186
adfuller(c)	0.113
adfuller(ct)	0.118
adfuller(ctt)	0.134
adfuller(nc)	0.130

ACF/PACF

acf1(d=0)	0.592
acf1(d=1)	-0.00825
acf1(d=2)	-0.340
acf10(d=0)	0.711
acf10(d=1)	0.291
acf10(d=2)	0.195
pacf5(d=0)	0.498
pacf5(d=1)	0.330
pacf5(d=2)	0.629
acf1(error)	0.330
acf10(error)	0.455
acf(season)	0.0660
pacf(season)	-0.139

Time Series Decomposition

Auto Correlation

Lag Plots

Seasonality plots

Smoothing

Production VS Mean

Series details

Mean	9.25m
Deviation	3.47m
Maximum	14.34m
Minimum	574.22k
Zeros	0
Missing	0
Infinites	0

Sample

2012-01	2012-02	2012-03	2012-04	2012-05	...	2022-05	2022-06	2022-07	2022-08	2022-09
14.34m	14.34m	14.34m	14.34m	14.34m	...	11.31m	11.31m	11.31m	11.31m	11.31m

Statistics

	Value
Mean	9.25m
Variance	12.04t
Standard deviation	3.47m
Median	10.29m
Median absolute deviation	1.03m
Coefficient of variation	0.375
Minimum	574.22k
25%	7.30m
50%	10.29m
75%	11.31m
Maximum	14.34m
Interquartile range	4.01m
Kurtosis	1.15
Skewness	-1.14

Statistical Tests

Test	Null Hypothesis	Reject	pvalue	Confidence
Levene	Variance is constant between 10 groups	No	nan	0.05
Ljung-Box	No autocorrelation among specified lag(22)	Yes	0.000	0.05
Jarque Bera	Data is normally distributed	Yes	0.000	0.05
Omnibus	Data is normally distributed	Yes	0.000	0.05
Augmented Dickey-Fuller	Data has unit root	No	0.318	0.05
Kwiatkowski-Phillips-Schmidt-Shin	Data is level stationary	No	0.100	0.05

Distribution

Main Features

trend	0.923
seasonality	0.126
entropy	0.488
entropy_acf	0.156
instability	1.15
lumpiness	0

Stationarity

kpss(c)	0.461
kpss(ct)	0.462
adfuller(c)	0.318
adfuller(ct)	0.645
adfuller(ctt)	0.766
adfuller(nc)	0.319

ACF/PACF

acf1(d=0)	0.948
acf1(d=1)	-0.000553
acf1(d=2)	-0.500
acf10(d=0)	5.34
acf10(d=1)	0.00000328
acf10(d=2)	0.250
pacf5(d=0)	0.902
pacf5(d=1)	0.00000158
pacf5(d=2)	0.491
acf1(error)	0.562
acf10(error)	0.588
acf(season)	0.379
pacf(season)	-0.0362

Time Series Decomposition

Auto Correlation

Lag Plots

Seasonality plots

Smoothing

Trade VS Mean

Series details

Mean	8.90k
Deviation	5.51k
Maximum	21.19k
Minimum	297.28
Zeros	0
Missing	0
Infinites	0

Sample

2012-01	2012-02	2012-03	2012-04	2012-05	...	2022-05	2022-06	2022-07	2022-08	2022-09
0.30k	0.30k	0.30k	0.30k	0.30k	...	9.12k	9.12k	9.12k	9.12k	9.12k

Statistics

	Value
Mean	8.90k
Variance	30.34m
Standard deviation	5.51k
Median	9.12k
Median absolute deviation	642.56
Coefficient of variation	0.619
Minimum	297.28
25%	4.19k
50%	9.12k
75%	9.12k
Maximum	21.19k
Interquartile range	4.93k
Kurtosis	0.222
Skewness	0.645

Statistical Tests

Test	Null Hypothesis	Reject	pvalue	Confidence
Levene	Variance is constant between 10 groups	No	nan	0.05
Ljung-Box	No autocorrelation among specified lag(22)	Yes	0.000	0.05
Jarque Bera	Data is normally distributed	Yes	0.010	0.05
Omnibus	Data is normally distributed	Yes	0.010	0.05
Augmented Dickey-Fuller	Data has unit root	Yes	0.047	0.05
Kwiatkowski-Phillips-Schmidt-Shin	Data is level stationary	No	0.100	0.05

Distribution

Main Features

trend	0.750
seasonality	0.0431
entropy	0.582
entropy_acf	0.388
instability	1.19
lumpiness	0

Stationarity

kpss(c)	0.0758
kpss(ct)	0.0579
adfuller(c)	0.0466
adfuller(ct)	0.195
adfuller(ctt)	0.455
adfuller(nc)	0.242

ACF/PACF

acf1(d=0)	0.872
acf1(d=1)	-0.000659
acf1(d=2)	-0.500
acf10(d=0)	2.22
acf10(d=1)	0.00000465
acf10(d=2)	0.250
pacf5(d=0)	0.783
pacf5(d=1)	0.00000224
pacf5(d=2)	0.491
acf1(error)	0.579
acf10(error)	0.571
acf(season)	-0.542
pacf(season)	-0.219

Time Series Decomposition

Auto Correlation

Lag Plots

Seasonality plots

Smoothing

Risk Normalized

Series details

Mean	176.30
Deviation	167.38
Maximum	765.50
Minimum	13.30
Zeros	0
Missing	0
Infinites	0

Sample

2012-01	2012-02	2012-03	2012-04	2012-05	...	2022-05	2022-06	2022-07	2022-08	2022-09
207.50	180.40	172.50	241.10	248.60	...	13.30	13.30	13.30	13.30	13.30

Statistics

	Value
Mean	176.30
Variance	28.02k
Standard deviation	167.38
Median	128.60
Median absolute deviation	100.80
Coefficient of variation	0.949
Minimum	13.30
25%	42.40
50%	128.60
75%	248.60
Maximum	765.50
Interquartile range	206.20
Kurtosis	1.49
Skewness	1.34

Statistical Tests

Test	Null Hypothesis	Reject	pvalue	Confidence
Levene	Variance is constant between 10 groups	Yes	0.000	0.05
Ljung-Box	No autocorrelation among specified lag(22)	Yes	0.000	0.05
Jarque Bera	Data is normally distributed	Yes	0.000	0.05
Omnibus	Data is normally distributed	Yes	0.000	0.05
Augmented Dickey-Fuller	Data has unit root	No	0.246	0.05
Kwiatkowski-Phillips-Schmidt-Shin	Data is level stationary	Yes	0.010	0.05

Distribution

Main Features

trend	0.810
seasonality	0.268
entropy	0.717
entropy_acf	0.271
instability	0.856
lumpiness	0.0627

Stationarity

kpss(c)	1.26
kpss(ct)	0.0700
adfuller(c)	0.246
adfuller(ct)	0.00159
adfuller(ctt)	0.00788
adfuller(nc)	0.152

ACF/PACF

acf1(d=0)	0.843
acf1(d=1)	-0.0534
acf1(d=2)	-0.431
acf10(d=0)	3.24
acf10(d=1)	0.167
acf10(d=2)	0.240
pacf5(d=0)	0.774
pacf5(d=1)	0.125
pacf5(d=2)	0.650
acf1(error)	0.422
acf10(error)	0.273
acf(season)	0.181
pacf(season)	-0.135

Time Series Decomposition

Auto Correlation

Lag Plots

Seasonality plots

Smoothing

Features

Relations

Pearson Correlations

Granger Causality Matrix

Critical value 0.05. Data differenced 2 times to try to achieve stationarity.

Granger Causality Graph

Critical value 0.05. Data differenced 2 times to try to achieve stationarity. Highlighted are the strongest granger-causality (lowest p-value) relationship of any given target variable.

Report generated in 0:00:20.705953 • tslumen